Overview

Dataset statistics

Number of variables44
Number of observations2216
Missing cells2653
Missing cells (%)2.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory761.9 KiB
Average record size in memory352.1 B

Variable types

Numeric7
Text13
Categorical17
Boolean4
Unsupported1
DateTime2

Alerts

Date_extracted has constant value ""Constant
School_code is highly overall correlated with Level_of_schoolingHigh correlation
AgeID is highly overall correlated with PostcodeHigh correlation
Postcode is highly overall correlated with AgeID and 11 other fieldsHigh correlation
Latitude is highly overall correlated with Longitude and 10 other fieldsHigh correlation
Longitude is highly overall correlated with Postcode and 9 other fieldsHigh correlation
Level_of_schooling is highly overall correlated with School_code and 1 other fieldsHigh correlation
School_subtype is highly overall correlated with Level_of_schoolingHigh correlation
Late_opening_school is highly overall correlated with Longitude and 8 other fieldsHigh correlation
Fed_electorate is highly overall correlated with Postcode and 13 other fieldsHigh correlation
Operational_directorate is highly overall correlated with Postcode and 10 other fieldsHigh correlation
Operational_directorate_office is highly overall correlated with Postcode and 12 other fieldsHigh correlation
Operational_directorate_office_phone is highly overall correlated with Postcode and 12 other fieldsHigh correlation
Operational_directorate_office_address is highly overall correlated with Postcode and 12 other fieldsHigh correlation
FACS_district is highly overall correlated with Postcode and 10 other fieldsHigh correlation
Local_health_district is highly overall correlated with Postcode and 12 other fieldsHigh correlation
AECG_region is highly overall correlated with Postcode and 13 other fieldsHigh correlation
ASGS_remoteness is highly overall correlated with Late_opening_school and 3 other fieldsHigh correlation
Assets unit is highly overall correlated with Postcode and 10 other fieldsHigh correlation
SA4 is highly overall correlated with Postcode and 13 other fieldsHigh correlation
Level_of_schooling is highly imbalanced (54.0%)Imbalance
Selective_school is highly imbalanced (89.3%)Imbalance
Opportunity_class is highly imbalanced (78.2%)Imbalance
School_specialty_type is highly imbalanced (91.3%)Imbalance
School_subtype is highly imbalanced (64.1%)Imbalance
Preschool_ind is highly imbalanced (73.1%)Imbalance
Distance_education is highly imbalanced (96.9%)Imbalance
Intensive_english_centre is highly imbalanced (93.8%)Imbalance
School_gender is highly imbalanced (89.3%)Imbalance
Late_opening_school is highly imbalanced (70.0%)Imbalance
Fax has 101 (4.6%) missing valuesMissing
latest_year_enrolment_FTE has 50 (2.3%) missing valuesMissing
Indigenous_pct has 50 (2.3%) missing valuesMissing
LBOTE_pct has 62 (2.8%) missing valuesMissing
ICSEA_value has 59 (2.7%) missing valuesMissing
Support_classes has 2216 (100.0%) missing valuesMissing
AECG_region has 24 (1.1%) missing valuesMissing
Assets unit has 38 (1.7%) missing valuesMissing
School_code has unique valuesUnique
School_name has unique valuesUnique
Longitude has unique valuesUnique
Support_classes is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-08-20 01:36:34.308801
Analysis finished2023-08-20 01:36:46.487129
Duration12.18 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

School_code
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct2216
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4290.3592
Minimum1001
Maximum8924
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2023-08-20T01:36:46.601795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1260.75
Q12409.75
median3956.5
Q35558.25
95-th percentile8517.25
Maximum8924
Range7923
Interquartile range (IQR)3148.5

Descriptive statistics

Standard deviation2349.9016
Coefficient of variation (CV)0.54771674
Kurtosis-0.7218706
Mean4290.3592
Median Absolute Deviation (MAD)1579
Skewness0.65933236
Sum9507436
Variance5522037.3
MonotonicityStrictly increasing
2023-08-20T01:36:46.806499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001 1
 
< 0.1%
4555 1
 
< 0.1%
4548 1
 
< 0.1%
4549 1
 
< 0.1%
4551 1
 
< 0.1%
4552 1
 
< 0.1%
4553 1
 
< 0.1%
4554 1
 
< 0.1%
4556 1
 
< 0.1%
4564 1
 
< 0.1%
Other values (2206) 2206
99.5%
ValueCountFrequency (%)
1001 1
< 0.1%
1002 1
< 0.1%
1003 1
< 0.1%
1007 1
< 0.1%
1008 1
< 0.1%
1009 1
< 0.1%
1015 1
< 0.1%
1016 1
< 0.1%
1017 1
< 0.1%
1019 1
< 0.1%
ValueCountFrequency (%)
8924 1
< 0.1%
8922 1
< 0.1%
8919 1
< 0.1%
8917 1
< 0.1%
8916 1
< 0.1%
8915 1
< 0.1%
8914 1
< 0.1%
8913 1
< 0.1%
8912 1
< 0.1%
8911 1
< 0.1%

AgeID
Real number (ℝ)

HIGH CORRELATION 

Distinct2214
Distinct (%)100.0%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean50555.43
Minimum6354
Maximum88561
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2023-08-20T01:36:47.005734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6354
5-th percentile45796.65
Q148155.25
median49675.5
Q350810.25
95-th percentile59847.9
Maximum88561
Range82207
Interquartile range (IQR)2655

Descriptive statistics

Standard deviation7825.7932
Coefficient of variation (CV)0.15479629
Kurtosis15.993242
Mean50555.43
Median Absolute Deviation (MAD)1161.5
Skewness-0.0093772809
Sum1.1192972 × 108
Variance61243039
MonotonicityNot monotonic
2023-08-20T01:36:47.183538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44402 1
 
< 0.1%
56239 1
 
< 0.1%
56256 1
 
< 0.1%
45332 1
 
< 0.1%
56214 1
 
< 0.1%
55123 1
 
< 0.1%
48552 1
 
< 0.1%
51080 1
 
< 0.1%
56274 1
 
< 0.1%
54746 1
 
< 0.1%
Other values (2204) 2204
99.5%
(Missing) 2
 
0.1%
ValueCountFrequency (%)
6354 1
< 0.1%
6356 1
< 0.1%
6398 1
< 0.1%
6399 1
< 0.1%
6405 1
< 0.1%
6432 1
< 0.1%
6441 1
< 0.1%
6451 1
< 0.1%
6496 1
< 0.1%
6497 1
< 0.1%
ValueCountFrequency (%)
88561 1
< 0.1%
88559 1
< 0.1%
88435 1
< 0.1%
88427 1
< 0.1%
88425 1
< 0.1%
88423 1
< 0.1%
88421 1
< 0.1%
88419 1
< 0.1%
88417 1
< 0.1%
88415 1
< 0.1%

School_name
Text

UNIQUE 

Distinct2216
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
2023-08-20T01:36:47.488177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length53
Mean length24.078069
Min length11

Characters and Unicode

Total characters53357
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2216 ?
Unique (%)100.0%

Sample

1st rowAbbotsford Public School
2nd rowAberdeen Public School
3rd rowAbermain Public School
4th rowAdaminaby Public School
5th rowAdamstown Public School
ValueCountFrequency (%)
school 2137
27.8%
public 1583
20.6%
high 359
 
4.7%
park 68
 
0.9%
central 60
 
0.8%
north 60
 
0.8%
west 56
 
0.7%
college 52
 
0.7%
south 49
 
0.6%
hill 42
 
0.5%
Other values (1754) 3208
41.8%
2023-08-20T01:36:48.017441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 6076
 
11.4%
5458
 
10.2%
l 5425
 
10.2%
c 4032
 
7.6%
i 3157
 
5.9%
h 3058
 
5.7%
a 2448
 
4.6%
S 2396
 
4.5%
u 2285
 
4.3%
e 2237
 
4.2%
Other values (45) 16785
31.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 40221
75.4%
Uppercase Letter 7660
 
14.4%
Space Separator 5458
 
10.2%
Dash Punctuation 10
 
< 0.1%
Other Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 6076
15.1%
l 5425
13.5%
c 4032
10.0%
i 3157
7.8%
h 3058
7.6%
a 2448
 
6.1%
u 2285
 
5.7%
e 2237
 
5.6%
r 1954
 
4.9%
b 1947
 
4.8%
Other values (16) 7602
18.9%
Uppercase Letter
ValueCountFrequency (%)
S 2396
31.3%
P 1800
23.5%
H 592
 
7.7%
C 487
 
6.4%
B 346
 
4.5%
W 255
 
3.3%
M 228
 
3.0%
G 189
 
2.5%
E 170
 
2.2%
T 167
 
2.2%
Other values (15) 1030
13.4%
Other Punctuation
ValueCountFrequency (%)
, 7
87.5%
' 1
 
12.5%
Space Separator
ValueCountFrequency (%)
5458
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 47881
89.7%
Common 5476
 
10.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 6076
12.7%
l 5425
 
11.3%
c 4032
 
8.4%
i 3157
 
6.6%
h 3058
 
6.4%
a 2448
 
5.1%
S 2396
 
5.0%
u 2285
 
4.8%
e 2237
 
4.7%
r 1954
 
4.1%
Other values (41) 14813
30.9%
Common
ValueCountFrequency (%)
5458
99.7%
- 10
 
0.2%
, 7
 
0.1%
' 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53357
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 6076
 
11.4%
5458
 
10.2%
l 5425
 
10.2%
c 4032
 
7.6%
i 3157
 
5.9%
h 3058
 
5.7%
a 2448
 
4.6%
S 2396
 
4.5%
u 2285
 
4.3%
e 2237
 
4.2%
Other values (45) 16785
31.5%

Street
Text

Distinct2000
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
2023-08-20T01:36:48.342587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length63
Median length41
Mean length13.616877
Min length5

Characters and Unicode

Total characters30175
Distinct characters69
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1863 ?
Unique (%)84.1%

Sample

1st row350 Great North Rd
2nd rowSegenhoe St
3rd rowGoulburn St
4th row9 Cosgrove Street
5th rowBryant St
ValueCountFrequency (%)
st 771
 
13.3%
rd 555
 
9.6%
street 212
 
3.7%
road 156
 
2.7%
ave 151
 
2.6%
drive 116
 
2.0%
90
 
1.6%
hwy 50
 
0.9%
avenue 43
 
0.7%
park 33
 
0.6%
Other values (2149) 3601
62.3%
2023-08-20T01:36:48.871699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3565
 
11.8%
e 2420
 
8.0%
t 1907
 
6.3%
a 1881
 
6.2%
r 1808
 
6.0%
o 1414
 
4.7%
n 1269
 
4.2%
d 1234
 
4.1%
S 1189
 
3.9%
l 1155
 
3.8%
Other values (59) 12333
40.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18766
62.2%
Uppercase Letter 5683
 
18.8%
Space Separator 3565
 
11.8%
Decimal Number 1925
 
6.4%
Dash Punctuation 129
 
0.4%
Other Punctuation 105
 
0.3%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2420
12.9%
t 1907
10.2%
a 1881
10.0%
r 1808
9.6%
o 1414
 
7.5%
n 1269
 
6.8%
d 1234
 
6.6%
l 1155
 
6.2%
i 1078
 
5.7%
s 654
 
3.5%
Other values (16) 3946
21.0%
Uppercase Letter
ValueCountFrequency (%)
S 1189
20.9%
R 903
15.9%
A 395
 
7.0%
C 362
 
6.4%
B 303
 
5.3%
H 256
 
4.5%
M 249
 
4.4%
P 239
 
4.2%
D 233
 
4.1%
W 227
 
4.0%
Other values (15) 1327
23.4%
Decimal Number
ValueCountFrequency (%)
1 394
20.5%
2 243
12.6%
3 200
10.4%
5 191
9.9%
0 177
9.2%
4 164
8.5%
7 152
 
7.9%
8 150
 
7.8%
6 136
 
7.1%
9 118
 
6.1%
Other Punctuation
ValueCountFrequency (%)
& 90
85.7%
' 8
 
7.6%
/ 4
 
3.8%
, 3
 
2.9%
Space Separator
ValueCountFrequency (%)
3565
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 129
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24449
81.0%
Common 5726
 
19.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2420
 
9.9%
t 1907
 
7.8%
a 1881
 
7.7%
r 1808
 
7.4%
o 1414
 
5.8%
n 1269
 
5.2%
d 1234
 
5.0%
S 1189
 
4.9%
l 1155
 
4.7%
i 1078
 
4.4%
Other values (41) 9094
37.2%
Common
ValueCountFrequency (%)
3565
62.3%
1 394
 
6.9%
2 243
 
4.2%
3 200
 
3.5%
5 191
 
3.3%
0 177
 
3.1%
4 164
 
2.9%
7 152
 
2.7%
8 150
 
2.6%
6 136
 
2.4%
Other values (8) 354
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3565
 
11.8%
e 2420
 
8.0%
t 1907
 
6.3%
a 1881
 
6.2%
r 1808
 
6.0%
o 1414
 
4.7%
n 1269
 
4.2%
d 1234
 
4.1%
S 1189
 
3.9%
l 1155
 
3.8%
Other values (59) 12333
40.9%
Distinct1618
Distinct (%)73.1%
Missing4
Missing (%)0.2%
Memory size17.4 KiB
2023-08-20T01:36:49.165242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length9.7902351
Min length3

Characters and Unicode

Total characters21656
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1270 ?
Unique (%)57.4%

Sample

1st rowAbbotsford
2nd rowAberdeen
3rd rowAbermain
4th rowADAMINABY
5th rowAdamstown
ValueCountFrequency (%)
park 56
 
2.0%
hill 49
 
1.7%
north 43
 
1.5%
hills 38
 
1.3%
wagga 30
 
1.0%
st 26
 
0.9%
west 25
 
0.9%
bay 25
 
0.9%
heights 22
 
0.8%
south 22
 
0.8%
Other values (1295) 2523
88.2%
2023-08-20T01:36:49.656824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2067
 
9.5%
a 1712
 
7.9%
e 1392
 
6.4%
r 1266
 
5.8%
o 1206
 
5.6%
l 1201
 
5.5%
n 1119
 
5.2%
i 800
 
3.7%
t 668
 
3.1%
s 540
 
2.5%
Other values (45) 9685
44.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13565
62.6%
Uppercase Letter 6018
27.8%
Space Separator 2067
 
9.5%
Dash Punctuation 5
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1712
12.6%
e 1392
10.3%
r 1266
 
9.3%
o 1206
 
8.9%
l 1201
 
8.9%
n 1119
 
8.2%
i 800
 
5.9%
t 668
 
4.9%
s 540
 
4.0%
u 465
 
3.4%
Other values (16) 3196
23.6%
Uppercase Letter
ValueCountFrequency (%)
A 464
 
7.7%
R 429
 
7.1%
E 422
 
7.0%
N 379
 
6.3%
B 373
 
6.2%
L 343
 
5.7%
T 336
 
5.6%
O 331
 
5.5%
C 327
 
5.4%
H 312
 
5.2%
Other values (16) 2302
38.3%
Space Separator
ValueCountFrequency (%)
2067
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Other Punctuation
ValueCountFrequency (%)
' 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19583
90.4%
Common 2073
 
9.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1712
 
8.7%
e 1392
 
7.1%
r 1266
 
6.5%
o 1206
 
6.2%
l 1201
 
6.1%
n 1119
 
5.7%
i 800
 
4.1%
t 668
 
3.4%
s 540
 
2.8%
u 465
 
2.4%
Other values (42) 9214
47.1%
Common
ValueCountFrequency (%)
2067
99.7%
- 5
 
0.2%
' 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2067
 
9.5%
a 1712
 
7.9%
e 1392
 
6.4%
r 1266
 
5.8%
o 1206
 
5.6%
l 1201
 
5.5%
n 1119
 
5.2%
i 800
 
3.7%
t 668
 
3.1%
s 540
 
2.5%
Other values (45) 9685
44.7%

Postcode
Real number (ℝ)

HIGH CORRELATION 

Distinct579
Distinct (%)26.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2416.8284
Minimum2000
Maximum4383
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2023-08-20T01:36:49.808126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2066.7
Q12204
median2420
Q32590
95-th percentile2820
Maximum4383
Range2383
Interquartile range (IQR)386

Descriptive statistics

Standard deviation247.51608
Coefficient of variation (CV)0.10241359
Kurtosis0.78570329
Mean2416.8284
Median Absolute Deviation (MAD)209
Skewness0.45060369
Sum5353275
Variance61264.211
MonotonicityNot monotonic
2023-08-20T01:36:49.995622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2480 32
 
1.4%
2250 26
 
1.2%
2170 24
 
1.1%
2560 24
 
1.1%
2770 21
 
0.9%
2340 19
 
0.9%
2259 18
 
0.8%
2650 17
 
0.8%
2145 17
 
0.8%
2450 17
 
0.8%
Other values (569) 2000
90.3%
ValueCountFrequency (%)
2000 3
0.1%
2007 1
 
< 0.1%
2008 1
 
< 0.1%
2010 5
0.2%
2011 3
0.1%
2015 2
 
0.1%
2017 1
 
< 0.1%
2018 2
 
0.1%
2019 4
0.2%
2020 2
 
0.1%
ValueCountFrequency (%)
4383 1
 
< 0.1%
3644 1
 
< 0.1%
2898 1
 
< 0.1%
2880 10
0.5%
2879 1
 
< 0.1%
2878 1
 
< 0.1%
2877 3
 
0.1%
2876 1
 
< 0.1%
2875 1
 
< 0.1%
2874 1
 
< 0.1%

Phone
Text

Distinct2210
Distinct (%)99.9%
Missing4
Missing (%)0.2%
Memory size17.4 KiB
2023-08-20T01:36:50.306223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length9
Mean length9.1641049
Min length8

Characters and Unicode

Total characters20271
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2209 ?
Unique (%)99.9%

Sample

1st row9713 6220
2nd row6543 7271
3rd row4930 4210
4th row6454 2265
5th row4957 1114
ValueCountFrequency (%)
02 56
 
1.2%
03 33
 
0.7%
07 17
 
0.4%
9622 16
 
0.4%
9773 15
 
0.3%
9607 15
 
0.3%
4284 14
 
0.3%
9631 13
 
0.3%
9602 13
 
0.3%
9625 13
 
0.3%
Other values (2489) 4329
95.5%
2023-08-20T01:36:50.727628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 2411
11.9%
2323
11.5%
4 2163
10.7%
9 2049
10.1%
2 2040
10.1%
5 1677
8.3%
1 1628
8.0%
3 1624
8.0%
7 1553
7.7%
8 1428
7.0%
Other values (3) 1375
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17942
88.5%
Space Separator 2323
 
11.5%
Open Punctuation 3
 
< 0.1%
Close Punctuation 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 2411
13.4%
4 2163
12.1%
9 2049
11.4%
2 2040
11.4%
5 1677
9.3%
1 1628
9.1%
3 1624
9.1%
7 1553
8.7%
8 1428
8.0%
0 1369
7.6%
Space Separator
ValueCountFrequency (%)
2323
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20271
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 2411
11.9%
2323
11.5%
4 2163
10.7%
9 2049
10.1%
2 2040
10.1%
5 1677
8.3%
1 1628
8.0%
3 1624
8.0%
7 1553
7.7%
8 1428
7.0%
Other values (3) 1375
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20271
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 2411
11.9%
2323
11.5%
4 2163
10.7%
9 2049
10.1%
2 2040
10.1%
5 1677
8.3%
1 1628
8.0%
3 1624
8.0%
7 1553
7.7%
8 1428
7.0%
Other values (3) 1375
6.8%
Distinct2215
Distinct (%)100.0%
Missing1
Missing (%)< 0.1%
Memory size17.4 KiB
2023-08-20T01:36:50.955707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length39
Mean length32.455079
Min length20

Characters and Unicode

Total characters71888
Distinct characters43
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2215 ?
Unique (%)100.0%

Sample

1st rowabbotsford-p.school@det.nsw.edu.au
2nd rowaberdeen-p.school@det.nsw.edu.au
3rd rowabermain-p.school@det.nsw.edu.au
4th rowadaminaby-p.school@det.nsw.edu.au
5th rowadamstown-p.school@det.nsw.edu.au
ValueCountFrequency (%)
abbotsford-p.school@det.nsw.edu.au 1
 
< 0.1%
alma-p.school@det.nsw.edu.au 1
 
< 0.1%
arcadia-p.school@det.nsw.edu.au 1
 
< 0.1%
appin-p.school@det.nsw.edu.au 1
 
< 0.1%
annandalen-p.school@det.nsw.edu.au 1
 
< 0.1%
annandale-p.school@det.nsw.edu.au 1
 
< 0.1%
annabay-p.school@det.nsw.edu.au 1
 
< 0.1%
alstonvill-p.school@det.nsw.edu.au 1
 
< 0.1%
alburywest-p.school@det.nsw.edu.au 1
 
< 0.1%
awaba-p.school@det.nsw.edu.au 1
 
< 0.1%
Other values (2205) 2205
99.5%
2023-08-20T01:36:51.347388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 8851
12.3%
e 6126
 
8.5%
o 5828
 
8.1%
s 5393
 
7.5%
d 5038
 
7.0%
u 4950
 
6.9%
a 4261
 
5.9%
n 3666
 
5.1%
l 3652
 
5.1%
h 3248
 
4.5%
Other values (33) 20875
29.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 58584
81.5%
Other Punctuation 11066
 
15.4%
Dash Punctuation 2208
 
3.1%
Uppercase Letter 30
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6126
10.5%
o 5828
9.9%
s 5393
9.2%
d 5038
 
8.6%
u 4950
 
8.4%
a 4261
 
7.3%
n 3666
 
6.3%
l 3652
 
6.2%
h 3248
 
5.5%
t 3187
 
5.4%
Other values (16) 13235
22.6%
Uppercase Letter
ValueCountFrequency (%)
C 7
23.3%
S 4
13.3%
B 3
10.0%
L 2
 
6.7%
H 2
 
6.7%
R 2
 
6.7%
N 2
 
6.7%
T 2
 
6.7%
J 1
 
3.3%
I 1
 
3.3%
Other values (4) 4
13.3%
Other Punctuation
ValueCountFrequency (%)
. 8851
80.0%
@ 2215
 
20.0%
Dash Punctuation
ValueCountFrequency (%)
- 2208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 58614
81.5%
Common 13274
 
18.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6126
10.5%
o 5828
9.9%
s 5393
9.2%
d 5038
 
8.6%
u 4950
 
8.4%
a 4261
 
7.3%
n 3666
 
6.3%
l 3652
 
6.2%
h 3248
 
5.5%
t 3187
 
5.4%
Other values (30) 13265
22.6%
Common
ValueCountFrequency (%)
. 8851
66.7%
@ 2215
 
16.7%
- 2208
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71888
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 8851
12.3%
e 6126
 
8.5%
o 5828
 
8.1%
s 5393
 
7.5%
d 5038
 
7.0%
u 4950
 
6.9%
a 4261
 
5.9%
n 3666
 
5.1%
l 3652
 
5.1%
h 3248
 
4.5%
Other values (33) 20875
29.0%
Distinct2214
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
2023-08-20T01:36:51.619907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length48
Mean length37.434567
Min length23

Characters and Unicode

Total characters82955
Distinct characters35
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2212 ?
Unique (%)99.8%

Sample

1st rowhttps://abbotsford-p.schools.nsw.gov.au
2nd rowhttps://aberdeen-p.schools.nsw.gov.au
3rd rowhttps://abermain-p.schools.nsw.gov.au
4th rowhttps://adaminaby-p.schools.nsw.gov.au
5th rowhttps://adamstown-p.schools.nsw.gov.au
ValueCountFrequency (%)
https://walgett-h.schools.nsw.gov.au 2
 
0.1%
available 2
 
0.1%
not 2
 
0.1%
currently 2
 
0.1%
https://alstonvill-p.schools.nsw.gov.au 1
 
< 0.1%
https://ardlethan-c.schools.nsw.gov.au 1
 
< 0.1%
https://arcadia-p.schools.nsw.gov.au 1
 
< 0.1%
https://appin-p.schools.nsw.gov.au 1
 
< 0.1%
https://annandalen-p.schools.nsw.gov.au 1
 
< 0.1%
https://annandale-p.schools.nsw.gov.au 1
 
< 0.1%
Other values (2206) 2206
99.4%
2023-08-20T01:36:51.992265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 9794
 
11.8%
. 8842
 
10.7%
o 8019
 
9.7%
h 5453
 
6.6%
t 5407
 
6.5%
/ 4484
 
5.4%
a 4256
 
5.1%
p 4111
 
5.0%
n 3656
 
4.4%
l 3652
 
4.4%
Other values (25) 25281
30.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 65214
78.6%
Other Punctuation 15540
 
18.7%
Dash Punctuation 2190
 
2.6%
Uppercase Letter 7
 
< 0.1%
Space Separator 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 9794
15.0%
o 8019
12.3%
h 5453
 
8.4%
t 5407
 
8.3%
a 4256
 
6.5%
p 4111
 
6.3%
n 3656
 
5.6%
l 3652
 
5.6%
g 2834
 
4.3%
c 2811
 
4.3%
Other values (16) 15221
23.3%
Uppercase Letter
ValueCountFrequency (%)
N 3
42.9%
P 2
28.6%
J 1
 
14.3%
H 1
 
14.3%
Other Punctuation
ValueCountFrequency (%)
. 8842
56.9%
/ 4484
28.9%
: 2214
 
14.2%
Dash Punctuation
ValueCountFrequency (%)
- 2190
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 65221
78.6%
Common 17734
 
21.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 9794
15.0%
o 8019
12.3%
h 5453
 
8.4%
t 5407
 
8.3%
a 4256
 
6.5%
p 4111
 
6.3%
n 3656
 
5.6%
l 3652
 
5.6%
g 2834
 
4.3%
c 2811
 
4.3%
Other values (20) 15228
23.3%
Common
ValueCountFrequency (%)
. 8842
49.9%
/ 4484
25.3%
: 2214
 
12.5%
- 2190
 
12.3%
4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 82955
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 9794
 
11.8%
. 8842
 
10.7%
o 8019
 
9.7%
h 5453
 
6.6%
t 5407
 
6.5%
/ 4484
 
5.4%
a 4256
 
5.1%
p 4111
 
5.0%
n 3656
 
4.4%
l 3652
 
4.4%
Other values (25) 25281
30.5%

Fax
Text

MISSING 

Distinct2113
Distinct (%)99.9%
Missing101
Missing (%)4.6%
Memory size17.4 KiB
2023-08-20T01:36:52.284842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length9
Mean length9.1352246
Min length8

Characters and Unicode

Total characters19321
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2111 ?
Unique (%)99.8%

Sample

1st row9712 1825
2nd row6543 7712
3rd row4930 4319
4th row6454 2552
5th row4956 2446
ValueCountFrequency (%)
02 34
 
0.8%
03 32
 
0.7%
4956 18
 
0.4%
9831 18
 
0.4%
07 17
 
0.4%
4942 15
 
0.3%
4628 15
 
0.3%
9896 15
 
0.3%
9838 15
 
0.3%
4950 14
 
0.3%
Other values (2437) 4127
95.5%
2023-08-20T01:36:52.768532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 2238
11.6%
2205
11.4%
9 2115
10.9%
4 2046
10.6%
2 1831
9.5%
3 1716
8.9%
5 1589
8.2%
8 1588
8.2%
7 1503
7.8%
1 1268
6.6%
Other values (3) 1222
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17112
88.6%
Space Separator 2205
 
11.4%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 2238
13.1%
9 2115
12.4%
4 2046
12.0%
2 1831
10.7%
3 1716
10.0%
5 1589
9.3%
8 1588
9.3%
7 1503
8.8%
1 1268
7.4%
0 1218
7.1%
Space Separator
ValueCountFrequency (%)
2205
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19321
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 2238
11.6%
2205
11.4%
9 2115
10.9%
4 2046
10.6%
2 1831
9.5%
3 1716
8.9%
5 1589
8.2%
8 1588
8.2%
7 1503
7.8%
1 1268
6.6%
Other values (3) 1222
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19321
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 2238
11.6%
2205
11.4%
9 2115
10.9%
4 2046
10.6%
2 1831
9.5%
3 1716
8.9%
5 1589
8.2%
8 1588
8.2%
7 1503
7.8%
1 1268
6.6%
Other values (3) 1222
6.3%

latest_year_enrolment_FTE
Real number (ℝ)

MISSING 

Distinct1002
Distinct (%)46.3%
Missing50
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean365.11159
Minimum2
Maximum2079
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2023-08-20T01:36:52.928523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile14
Q1101.25
median293
Q3532
95-th percentile985.675
Maximum2079
Range2077
Interquartile range (IQR)430.75

Descriptive statistics

Standard deviation323.15849
Coefficient of variation (CV)0.88509514
Kurtosis1.8723538
Mean365.11159
Median Absolute Deviation (MAD)215.8
Skewness1.2403897
Sum790831.7
Variance104431.41
MonotonicityNot monotonic
2023-08-20T01:36:53.066196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 17
 
0.8%
12 16
 
0.7%
22 14
 
0.6%
16 13
 
0.6%
35 13
 
0.6%
10 13
 
0.6%
14 13
 
0.6%
26 13
 
0.6%
23 12
 
0.5%
29 11
 
0.5%
Other values (992) 2031
91.7%
(Missing) 50
 
2.3%
ValueCountFrequency (%)
2 2
 
0.1%
3 1
 
< 0.1%
4 4
 
0.2%
5 2
 
0.1%
6 6
 
0.3%
7 9
0.4%
8 8
0.4%
9 11
0.5%
10 13
0.6%
11 17
0.8%
ValueCountFrequency (%)
2079 1
< 0.1%
2049.3 1
< 0.1%
2044.4 1
< 0.1%
1948 1
< 0.1%
1874 1
< 0.1%
1686.8 1
< 0.1%
1637.4 1
< 0.1%
1606.8 1
< 0.1%
1601 1
< 0.1%
1578.6 1
< 0.1%

Indigenous_pct
Text

MISSING 

Distinct84
Distinct (%)3.9%
Missing50
Missing (%)2.3%
Memory size17.4 KiB
2023-08-20T01:36:53.216394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.1925208
Min length2

Characters and Unicode

Total characters6915
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.6%

Sample

1st row2.0
2nd row22.0
3rd row26.0
4th row0.0
5th row8.0
ValueCountFrequency (%)
np 481
22.2%
0.0 125
 
5.8%
2.0 103
 
4.8%
1.0 98
 
4.5%
3.0 81
 
3.7%
4.0 80
 
3.7%
7.0 70
 
3.2%
6.0 67
 
3.1%
9.0 64
 
3.0%
5.0 57
 
2.6%
Other values (74) 940
43.4%
2023-08-20T01:36:53.527143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1940
28.1%
. 1685
24.4%
1 650
 
9.4%
n 481
 
7.0%
p 481
 
7.0%
2 421
 
6.1%
3 284
 
4.1%
4 203
 
2.9%
5 179
 
2.6%
6 166
 
2.4%
Other values (3) 425
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4268
61.7%
Other Punctuation 1685
 
24.4%
Lowercase Letter 962
 
13.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1940
45.5%
1 650
 
15.2%
2 421
 
9.9%
3 284
 
6.7%
4 203
 
4.8%
5 179
 
4.2%
6 166
 
3.9%
7 159
 
3.7%
9 138
 
3.2%
8 128
 
3.0%
Lowercase Letter
ValueCountFrequency (%)
n 481
50.0%
p 481
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1685
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5953
86.1%
Latin 962
 
13.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1940
32.6%
. 1685
28.3%
1 650
 
10.9%
2 421
 
7.1%
3 284
 
4.8%
4 203
 
3.4%
5 179
 
3.0%
6 166
 
2.8%
7 159
 
2.7%
9 138
 
2.3%
Latin
ValueCountFrequency (%)
n 481
50.0%
p 481
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6915
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1940
28.1%
. 1685
24.4%
1 650
 
9.4%
n 481
 
7.0%
p 481
 
7.0%
2 421
 
6.1%
3 284
 
4.1%
4 203
 
2.9%
5 179
 
2.6%
6 166
 
2.4%
Other values (3) 425
 
6.1%

LBOTE_pct
Text

MISSING 

Distinct101
Distinct (%)4.7%
Missing62
Missing (%)2.8%
Memory size17.4 KiB
2023-08-20T01:36:53.733022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.3909006
Min length2

Characters and Unicode

Total characters7304
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row41.0
2nd rownp
3rd rownp
4th rownp
5th row14.0
ValueCountFrequency (%)
np 314
 
14.6%
0.0 184
 
8.5%
6.0 95
 
4.4%
5.0 79
 
3.7%
7.0 72
 
3.3%
8.0 70
 
3.2%
4.0 66
 
3.1%
9.0 56
 
2.6%
10.0 50
 
2.3%
3.0 46
 
2.1%
Other values (91) 1122
52.1%
2023-08-20T01:36:54.070804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2166
29.7%
. 1840
25.2%
1 434
 
5.9%
n 314
 
4.3%
p 314
 
4.3%
3 301
 
4.1%
6 298
 
4.1%
2 294
 
4.0%
5 276
 
3.8%
8 270
 
3.7%
Other values (3) 797
 
10.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4836
66.2%
Other Punctuation 1840
 
25.2%
Lowercase Letter 628
 
8.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2166
44.8%
1 434
 
9.0%
3 301
 
6.2%
6 298
 
6.2%
2 294
 
6.1%
5 276
 
5.7%
8 270
 
5.6%
4 270
 
5.6%
7 269
 
5.6%
9 258
 
5.3%
Lowercase Letter
ValueCountFrequency (%)
n 314
50.0%
p 314
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1840
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6676
91.4%
Latin 628
 
8.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2166
32.4%
. 1840
27.6%
1 434
 
6.5%
3 301
 
4.5%
6 298
 
4.5%
2 294
 
4.4%
5 276
 
4.1%
8 270
 
4.0%
4 270
 
4.0%
7 269
 
4.0%
Latin
ValueCountFrequency (%)
n 314
50.0%
p 314
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2166
29.7%
. 1840
25.2%
1 434
 
5.9%
n 314
 
4.3%
p 314
 
4.3%
3 301
 
4.1%
6 298
 
4.1%
2 294
 
4.0%
5 276
 
3.8%
8 270
 
3.7%
Other values (3) 797
 
10.9%

ICSEA_value
Real number (ℝ)

MISSING 

Distinct430
Distinct (%)19.9%
Missing59
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean979.49003
Minimum586
Maximum1225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2023-08-20T01:36:54.247928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum586
5-th percentile828
Q1922
median974
Q31043
95-th percentile1139
Maximum1225
Range639
Interquartile range (IQR)121

Descriptive statistics

Standard deviation95.388613
Coefficient of variation (CV)0.097385997
Kurtosis0.67346853
Mean979.49003
Median Absolute Deviation (MAD)59
Skewness-0.28980231
Sum2112760
Variance9098.9875
MonotonicityNot monotonic
2023-08-20T01:36:54.398252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
972 17
 
0.8%
994 17
 
0.8%
985 17
 
0.8%
959 16
 
0.7%
978 15
 
0.7%
922 15
 
0.7%
935 15
 
0.7%
944 15
 
0.7%
937 14
 
0.6%
1009 14
 
0.6%
Other values (420) 2002
90.3%
(Missing) 59
 
2.7%
ValueCountFrequency (%)
586 1
< 0.1%
595 1
< 0.1%
622 2
0.1%
631 1
< 0.1%
640 2
0.1%
646 1
< 0.1%
650 1
< 0.1%
652 1
< 0.1%
662 1
< 0.1%
666 1
< 0.1%
ValueCountFrequency (%)
1225 1
< 0.1%
1222 1
< 0.1%
1211 1
< 0.1%
1207 1
< 0.1%
1200 1
< 0.1%
1196 1
< 0.1%
1194 1
< 0.1%
1192 1
< 0.1%
1191 1
< 0.1%
1189 1
< 0.1%

Level_of_schooling
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
Primary School
1589 
Secondary School
405 
Schools for Specific Purposes
 
117
Central/Community School
 
67
Environmental Education Centre
 
22
Other values (2)
 
16

Length

Max length30
Median length14
Mean length15.616877
Min length12

Characters and Unicode

Total characters34607
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrimary School
2nd rowPrimary School
3rd rowPrimary School
4th rowPrimary School
5th rowPrimary School

Common Values

ValueCountFrequency (%)
Primary School 1589
71.7%
Secondary School 405
 
18.3%
Schools for Specific Purposes 117
 
5.3%
Central/Community School 67
 
3.0%
Environmental Education Centre 22
 
1.0%
Infants School 14
 
0.6%
Other School 2
 
0.1%

Length

2023-08-20T01:36:54.543127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-20T01:36:54.721101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
school 2077
44.3%
primary 1589
33.9%
secondary 405
 
8.6%
schools 117
 
2.5%
for 117
 
2.5%
specific 117
 
2.5%
purposes 117
 
2.5%
central/community 67
 
1.4%
environmental 22
 
0.5%
education 22
 
0.5%
Other values (3) 38
 
0.8%

Most occurring characters

ValueCountFrequency (%)
o 5138
14.8%
r 3930
11.4%
c 2855
8.2%
S 2716
 
7.8%
2472
 
7.1%
l 2283
 
6.6%
h 2196
 
6.3%
a 2119
 
6.1%
y 2061
 
6.0%
i 1934
 
5.6%
Other values (16) 6903
19.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 27430
79.3%
Uppercase Letter 4638
 
13.4%
Space Separator 2472
 
7.1%
Other Punctuation 67
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 5138
18.7%
r 3930
14.3%
c 2855
10.4%
l 2283
8.3%
h 2196
8.0%
a 2119
7.7%
y 2061
7.5%
i 1934
 
7.1%
m 1745
 
6.4%
e 774
 
2.8%
Other values (8) 2395
8.7%
Uppercase Letter
ValueCountFrequency (%)
S 2716
58.6%
P 1706
36.8%
C 156
 
3.4%
E 44
 
0.9%
I 14
 
0.3%
O 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
2472
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 67
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 32068
92.7%
Common 2539
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 5138
16.0%
r 3930
12.3%
c 2855
8.9%
S 2716
8.5%
l 2283
7.1%
h 2196
6.8%
a 2119
6.6%
y 2061
6.4%
i 1934
 
6.0%
m 1745
 
5.4%
Other values (14) 5091
15.9%
Common
ValueCountFrequency (%)
2472
97.4%
/ 67
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34607
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 5138
14.8%
r 3930
11.4%
c 2855
8.2%
S 2716
 
7.8%
2472
 
7.1%
l 2283
 
6.6%
h 2196
 
6.3%
a 2119
 
6.1%
y 2061
 
6.0%
i 1934
 
5.6%
Other values (16) 6903
19.9%

Selective_school
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
Not Selective
2169 
Partially Selective
 
26
Fully Selective
 
21

Length

Max length19
Median length13
Mean length13.08935
Min length13

Characters and Unicode

Total characters29006
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Selective
2nd rowNot Selective
3rd rowNot Selective
4th rowNot Selective
5th rowNot Selective

Common Values

ValueCountFrequency (%)
Not Selective 2169
97.9%
Partially Selective 26
 
1.2%
Fully Selective 21
 
0.9%

Length

2023-08-20T01:36:54.863108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-20T01:36:54.979678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
selective 2216
50.0%
not 2169
48.9%
partially 26
 
0.6%
fully 21
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e 6648
22.9%
t 4411
15.2%
l 2310
 
8.0%
i 2242
 
7.7%
2216
 
7.6%
S 2216
 
7.6%
c 2216
 
7.6%
v 2216
 
7.6%
N 2169
 
7.5%
o 2169
 
7.5%
Other values (6) 193
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22358
77.1%
Uppercase Letter 4432
 
15.3%
Space Separator 2216
 
7.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6648
29.7%
t 4411
19.7%
l 2310
 
10.3%
i 2242
 
10.0%
c 2216
 
9.9%
v 2216
 
9.9%
o 2169
 
9.7%
a 52
 
0.2%
y 47
 
0.2%
r 26
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
S 2216
50.0%
N 2169
48.9%
P 26
 
0.6%
F 21
 
0.5%
Space Separator
ValueCountFrequency (%)
2216
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 26790
92.4%
Common 2216
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6648
24.8%
t 4411
16.5%
l 2310
 
8.6%
i 2242
 
8.4%
S 2216
 
8.3%
c 2216
 
8.3%
v 2216
 
8.3%
N 2169
 
8.1%
o 2169
 
8.1%
a 52
 
0.2%
Other values (5) 141
 
0.5%
Common
ValueCountFrequency (%)
2216
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29006
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6648
22.9%
t 4411
15.2%
l 2310
 
8.0%
i 2242
 
7.7%
2216
 
7.6%
S 2216
 
7.6%
c 2216
 
7.6%
v 2216
 
7.6%
N 2169
 
7.5%
o 2169
 
7.5%
Other values (6) 193
 
0.7%

Opportunity_class
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
False
2139 
True
 
77
ValueCountFrequency (%)
False 2139
96.5%
True 77
 
3.5%
2023-08-20T01:36:55.082784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

School_specialty_type
Categorical

IMBALANCE 

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
Comprehensive
2136 
Junior College
 
18
Senior College
 
16
Technology
 
10
Performing Arts
 
8
Other values (10)
 
28

Length

Max length18
Median length13
Mean length12.976083
Min length5

Characters and Unicode

Total characters28755
Distinct characters34
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.3%

Sample

1st rowComprehensive
2nd rowComprehensive
3rd rowComprehensive
4th rowComprehensive
5th rowComprehensive

Common Values

ValueCountFrequency (%)
Comprehensive 2136
96.4%
Junior College 18
 
0.8%
Senior College 16
 
0.7%
Technology 10
 
0.5%
Performing Arts 8
 
0.4%
Language 7
 
0.3%
Sports 7
 
0.3%
Agricultural 5
 
0.2%
Distance Education 3
 
0.1%
Intensive English 1
 
< 0.1%
Other values (5) 5
 
0.2%

Length

2023-08-20T01:36:55.240104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
comprehensive 2136
94.3%
college 34
 
1.5%
junior 18
 
0.8%
senior 16
 
0.7%
technology 12
 
0.5%
arts 10
 
0.4%
performing 8
 
0.4%
language 7
 
0.3%
sports 7
 
0.3%
agricultural 5
 
0.2%
Other values (9) 13
 
0.6%

Most occurring characters

ValueCountFrequency (%)
e 6528
22.7%
o 2246
 
7.8%
r 2217
 
7.7%
n 2207
 
7.7%
i 2194
 
7.6%
C 2171
 
7.5%
s 2159
 
7.5%
h 2150
 
7.5%
m 2144
 
7.5%
p 2143
 
7.5%
Other values (24) 2596
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26439
91.9%
Uppercase Letter 2266
 
7.9%
Space Separator 50
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6528
24.7%
o 2246
 
8.5%
r 2217
 
8.4%
n 2207
 
8.3%
i 2194
 
8.3%
s 2159
 
8.2%
h 2150
 
8.1%
m 2144
 
8.1%
p 2143
 
8.1%
v 2138
 
8.1%
Other values (9) 313
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
C 2171
95.8%
S 23
 
1.0%
J 18
 
0.8%
A 15
 
0.7%
T 12
 
0.5%
P 8
 
0.4%
L 7
 
0.3%
E 4
 
0.2%
D 3
 
0.1%
I 1
 
< 0.1%
Other values (4) 4
 
0.2%
Space Separator
ValueCountFrequency (%)
50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 28705
99.8%
Common 50
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6528
22.7%
o 2246
 
7.8%
r 2217
 
7.7%
n 2207
 
7.7%
i 2194
 
7.6%
C 2171
 
7.6%
s 2159
 
7.5%
h 2150
 
7.5%
m 2144
 
7.5%
p 2143
 
7.5%
Other values (23) 2546
 
8.9%
Common
ValueCountFrequency (%)
50
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6528
22.7%
o 2246
 
7.8%
r 2217
 
7.7%
n 2207
 
7.7%
i 2194
 
7.6%
C 2171
 
7.5%
s 2159
 
7.5%
h 2150
 
7.5%
m 2144
 
7.5%
p 2143
 
7.5%
Other values (24) 2596
 
9.0%

School_subtype
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct18
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
Kinder to Year 6
1589 
Year 7 to Year 12
371 
Kinder to Year 12
 
67
Medium / High Support needs
 
64
Environmental Education Centre (formerly Field Studies Centres)
 
22
Other values (13)
 
103

Length

Max length63
Median length16
Mean length17.125903
Min length15

Characters and Unicode

Total characters37951
Distinct characters44
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowKinder to Year 6
2nd rowKinder to Year 6
3rd rowKinder to Year 6
4th rowKinder to Year 6
5th rowKinder to Year 6

Common Values

ValueCountFrequency (%)
Kinder to Year 6 1589
71.7%
Year 7 to Year 12 371
 
16.7%
Kinder to Year 12 67
 
3.0%
Medium / High Support needs 64
 
2.9%
Environmental Education Centre (formerly Field Studies Centres) 22
 
1.0%
Behaviour Disorder 22
 
1.0%
Year 7 to Year 10 16
 
0.7%
Kinder to Year 2 14
 
0.6%
Year 11 to Year 12 12
 
0.5%
Emotional disturbance 12
 
0.5%
Other values (8) 27
 
1.2%

Length

2023-08-20T01:36:55.592689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
year 2480
26.6%
to 2076
22.3%
kinder 1670
17.9%
6 1589
17.1%
12 454
 
4.9%
7 389
 
4.2%
medium 64
 
0.7%
64
 
0.7%
high 64
 
0.7%
support 64
 
0.7%
Other values (35) 393
 
4.2%

Most occurring characters

ValueCountFrequency (%)
7091
18.7%
e 4621
12.2%
r 4409
11.6%
a 2596
 
6.8%
Y 2480
 
6.5%
t 2312
 
6.1%
o 2309
 
6.1%
i 2002
 
5.3%
n 1909
 
5.0%
d 1904
 
5.0%
Other values (34) 6318
16.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23214
61.2%
Space Separator 7091
 
18.7%
Uppercase Letter 4570
 
12.0%
Decimal Number 2966
 
7.8%
Other Punctuation 64
 
0.2%
Open Punctuation 23
 
0.1%
Close Punctuation 23
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4621
19.9%
r 4409
19.0%
a 2596
11.2%
t 2312
10.0%
o 2309
9.9%
i 2002
8.6%
n 1909
8.2%
d 1904
8.2%
u 221
 
1.0%
s 184
 
0.8%
Other values (10) 747
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
Y 2480
54.3%
K 1670
36.5%
S 96
 
2.1%
H 74
 
1.6%
M 64
 
1.4%
E 57
 
1.2%
C 45
 
1.0%
D 25
 
0.5%
F 22
 
0.5%
B 22
 
0.5%
Other values (4) 15
 
0.3%
Decimal Number
ValueCountFrequency (%)
6 1589
53.6%
1 498
 
16.8%
2 468
 
15.8%
7 389
 
13.1%
0 20
 
0.7%
9 2
 
0.1%
Space Separator
ValueCountFrequency (%)
7091
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 64
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27784
73.2%
Common 10167
 
26.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4621
16.6%
r 4409
15.9%
a 2596
9.3%
Y 2480
8.9%
t 2312
8.3%
o 2309
8.3%
i 2002
7.2%
n 1909
6.9%
d 1904
6.9%
K 1670
 
6.0%
Other values (24) 1572
 
5.7%
Common
ValueCountFrequency (%)
7091
69.7%
6 1589
 
15.6%
1 498
 
4.9%
2 468
 
4.6%
7 389
 
3.8%
/ 64
 
0.6%
( 23
 
0.2%
) 23
 
0.2%
0 20
 
0.2%
9 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37951
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7091
18.7%
e 4621
12.2%
r 4409
11.6%
a 2596
 
6.8%
Y 2480
 
6.5%
t 2312
 
6.1%
o 2309
 
6.1%
i 2002
 
5.3%
n 1909
 
5.0%
d 1904
 
5.0%
Other values (34) 6318
16.6%

Support_classes
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2216
Missing (%)100.0%
Memory size17.4 KiB

Preschool_ind
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
False
2114 
True
 
102
ValueCountFrequency (%)
False 2114
95.4%
True 102
 
4.6%
2023-08-20T01:36:55.732310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Distance_education
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
N
2205 
S
 
9
C
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2216
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 2205
99.5%
S 9
 
0.4%
C 2
 
0.1%

Length

2023-08-20T01:36:55.872299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-20T01:36:56.009034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 2205
99.5%
s 9
 
0.4%
c 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
N 2205
99.5%
S 9
 
0.4%
C 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2216
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 2205
99.5%
S 9
 
0.4%
C 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2216
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 2205
99.5%
S 9
 
0.4%
C 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 2205
99.5%
S 9
 
0.4%
C 2
 
0.1%

Intensive_english_centre
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
False
2200 
True
 
16
ValueCountFrequency (%)
False 2200
99.3%
True 16
 
0.7%
2023-08-20T01:36:56.139785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

School_gender
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
Coed
2169 
Girls
 
24
Boys
 
23

Length

Max length5
Median length4
Mean length4.0108303
Min length4

Characters and Unicode

Total characters8888
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCoed
2nd rowCoed
3rd rowCoed
4th rowCoed
5th rowCoed

Common Values

ValueCountFrequency (%)
Coed 2169
97.9%
Girls 24
 
1.1%
Boys 23
 
1.0%

Length

2023-08-20T01:36:56.283815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-20T01:36:56.424765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
coed 2169
97.9%
girls 24
 
1.1%
boys 23
 
1.0%

Most occurring characters

ValueCountFrequency (%)
o 2192
24.7%
C 2169
24.4%
e 2169
24.4%
d 2169
24.4%
s 47
 
0.5%
G 24
 
0.3%
i 24
 
0.3%
r 24
 
0.3%
l 24
 
0.3%
B 23
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6672
75.1%
Uppercase Letter 2216
 
24.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 2192
32.9%
e 2169
32.5%
d 2169
32.5%
s 47
 
0.7%
i 24
 
0.4%
r 24
 
0.4%
l 24
 
0.4%
y 23
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
C 2169
97.9%
G 24
 
1.1%
B 23
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8888
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 2192
24.7%
C 2169
24.4%
e 2169
24.4%
d 2169
24.4%
s 47
 
0.5%
G 24
 
0.3%
i 24
 
0.3%
r 24
 
0.3%
l 24
 
0.3%
B 23
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8888
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 2192
24.7%
C 2169
24.4%
e 2169
24.4%
d 2169
24.4%
s 47
 
0.5%
G 24
 
0.3%
i 24
 
0.3%
r 24
 
0.3%
l 24
 
0.3%
B 23
 
0.3%

Late_opening_school
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
False
2098 
True
 
118
ValueCountFrequency (%)
False 2098
94.7%
True 118
 
5.3%
2023-08-20T01:36:56.557017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct787
Distinct (%)35.5%
Missing1
Missing (%)< 0.1%
Memory size17.4 KiB
Minimum1849-01-01 00:00:00
Maximum2023-07-17 00:00:00
2023-08-20T01:36:56.707083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:56.887225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

LGA
Text

Distinct130
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
2023-08-20T01:36:57.149063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length24
Mean length15.19991
Min length7

Characters and Unicode

Total characters33683
Distinct characters52
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCanada Bay (A)
2nd rowUpper Hunter Shire (A)
3rd rowCessnock (C)
4th rowSnowy Monaro Regional (A)
5th rowNewcastle (C)
ValueCountFrequency (%)
a 1270
23.9%
c 942
 
17.7%
regional 130
 
2.4%
shire 127
 
2.4%
blacktown 83
 
1.6%
central 81
 
1.5%
coast 77
 
1.4%
canterbury-bankstown 75
 
1.4%
lake 68
 
1.3%
macquarie 68
 
1.3%
Other values (144) 2397
45.1%
2023-08-20T01:36:57.612625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3102
 
9.2%
a 2474
 
7.3%
e 2437
 
7.2%
) 2212
 
6.6%
( 2212
 
6.6%
r 1860
 
5.5%
n 1829
 
5.4%
l 1603
 
4.8%
o 1561
 
4.6%
C 1464
 
4.3%
Other values (42) 12929
38.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20431
60.7%
Uppercase Letter 5498
 
16.3%
Space Separator 3102
 
9.2%
Close Punctuation 2212
 
6.6%
Open Punctuation 2212
 
6.6%
Dash Punctuation 224
 
0.7%
Connector Punctuation 4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 1464
26.6%
A 1300
23.6%
B 385
 
7.0%
S 344
 
6.3%
W 255
 
4.6%
M 253
 
4.6%
R 224
 
4.1%
L 196
 
3.6%
P 193
 
3.5%
H 192
 
3.5%
Other values (14) 692
12.6%
Lowercase Letter
ValueCountFrequency (%)
a 2474
12.1%
e 2437
11.9%
r 1860
 
9.1%
n 1829
 
9.0%
l 1603
 
7.8%
o 1561
 
7.6%
t 1194
 
5.8%
i 1148
 
5.6%
s 810
 
4.0%
u 657
 
3.2%
Other values (13) 4858
23.8%
Space Separator
ValueCountFrequency (%)
3102
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2212
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2212
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 224
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 25929
77.0%
Common 7754
 
23.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2474
 
9.5%
e 2437
 
9.4%
r 1860
 
7.2%
n 1829
 
7.1%
l 1603
 
6.2%
o 1561
 
6.0%
C 1464
 
5.6%
A 1300
 
5.0%
t 1194
 
4.6%
i 1148
 
4.4%
Other values (37) 9059
34.9%
Common
ValueCountFrequency (%)
3102
40.0%
) 2212
28.5%
( 2212
28.5%
- 224
 
2.9%
_ 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33683
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3102
 
9.2%
a 2474
 
7.3%
e 2437
 
7.2%
) 2212
 
6.6%
( 2212
 
6.6%
r 1860
 
5.5%
n 1829
 
5.4%
l 1603
 
4.8%
o 1561
 
4.6%
C 1464
 
4.3%
Other values (42) 12929
38.4%
Distinct93
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
2023-08-20T01:36:57.900363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length13
Mean length8.9584838
Min length4

Characters and Unicode

Total characters19852
Distinct characters45
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDrummoyne
2nd rowUpper Hunter
3rd rowCessnock
4th rowMonaro
5th rowNewcastle
ValueCountFrequency (%)
wagga 72
 
2.7%
barwon 69
 
2.6%
macquarie 68
 
2.5%
cootamundra 62
 
2.3%
tablelands 61
 
2.3%
northern 61
 
2.3%
lismore 59
 
2.2%
murray 58
 
2.1%
goulburn 45
 
1.7%
tamworth 45
 
1.7%
Other values (97) 2104
77.8%
2023-08-20T01:36:58.370150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2070
 
10.4%
r 1711
 
8.6%
e 1580
 
8.0%
o 1467
 
7.4%
n 1325
 
6.7%
l 1283
 
6.5%
t 1057
 
5.3%
u 786
 
4.0%
s 769
 
3.9%
i 691
 
3.5%
Other values (35) 7113
35.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16660
83.9%
Uppercase Letter 2704
 
13.6%
Space Separator 488
 
2.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2070
12.4%
r 1711
10.3%
e 1580
9.5%
o 1467
 
8.8%
n 1325
 
8.0%
l 1283
 
7.7%
t 1057
 
6.3%
u 786
 
4.7%
s 769
 
4.6%
i 691
 
4.1%
Other values (14) 3921
23.5%
Uppercase Letter
ValueCountFrequency (%)
C 356
13.2%
M 307
11.4%
B 257
9.5%
H 252
9.3%
W 230
 
8.5%
L 191
 
7.1%
T 158
 
5.8%
S 135
 
5.0%
P 110
 
4.1%
N 104
 
3.8%
Other values (10) 604
22.3%
Space Separator
ValueCountFrequency (%)
488
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19364
97.5%
Common 488
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2070
 
10.7%
r 1711
 
8.8%
e 1580
 
8.2%
o 1467
 
7.6%
n 1325
 
6.8%
l 1283
 
6.6%
t 1057
 
5.5%
u 786
 
4.1%
s 769
 
4.0%
i 691
 
3.6%
Other values (34) 6625
34.2%
Common
ValueCountFrequency (%)
488
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19852
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2070
 
10.4%
r 1711
 
8.6%
e 1580
 
8.0%
o 1467
 
7.4%
n 1325
 
6.7%
l 1283
 
6.5%
t 1057
 
5.3%
u 786
 
4.0%
s 769
 
3.9%
i 691
 
3.5%
Other values (35) 7113
35.8%
Distinct93
Distinct (%)4.2%
Missing8
Missing (%)0.4%
Memory size17.4 KiB
2023-08-20T01:36:58.651351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length13
Mean length8.8940217
Min length4

Characters and Unicode

Total characters19638
Distinct characters46
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDrummoyne
2nd rowUpper Hunter
3rd rowCessnock
4th rowMonaro
5th rowNewcastle
ValueCountFrequency (%)
macquarie 78
 
2.9%
wagga 72
 
2.7%
barwon 69
 
2.6%
cootamundra 61
 
2.3%
tablelands 58
 
2.2%
northern 58
 
2.2%
murray 57
 
2.1%
lismore 55
 
2.0%
hills 52
 
1.9%
hunter 45
 
1.7%
Other values (96) 2081
77.5%
2023-08-20T01:36:59.119844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2119
 
10.8%
r 1682
 
8.6%
e 1556
 
7.9%
o 1410
 
7.2%
n 1291
 
6.6%
l 1281
 
6.5%
t 989
 
5.0%
u 818
 
4.2%
s 737
 
3.8%
i 686
 
3.5%
Other values (36) 7069
36.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16444
83.7%
Uppercase Letter 2686
 
13.7%
Space Separator 478
 
2.4%
Dash Punctuation 30
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2119
12.9%
r 1682
10.2%
e 1556
9.5%
o 1410
 
8.6%
n 1291
 
7.9%
l 1281
 
7.8%
t 989
 
6.0%
u 818
 
5.0%
s 737
 
4.5%
i 686
 
4.2%
Other values (14) 3875
23.6%
Uppercase Letter
ValueCountFrequency (%)
C 340
12.7%
M 326
12.1%
H 265
9.9%
B 260
9.7%
L 197
 
7.3%
W 189
 
7.0%
T 150
 
5.6%
S 150
 
5.6%
P 111
 
4.1%
N 101
 
3.8%
Other values (10) 597
22.2%
Space Separator
ValueCountFrequency (%)
478
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19130
97.4%
Common 508
 
2.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2119
 
11.1%
r 1682
 
8.8%
e 1556
 
8.1%
o 1410
 
7.4%
n 1291
 
6.7%
l 1281
 
6.7%
t 989
 
5.2%
u 818
 
4.3%
s 737
 
3.9%
i 686
 
3.6%
Other values (34) 6561
34.3%
Common
ValueCountFrequency (%)
478
94.1%
- 30
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19638
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2119
 
10.8%
r 1682
 
8.6%
e 1556
 
7.9%
o 1410
 
7.2%
n 1291
 
6.6%
l 1281
 
6.5%
t 989
 
5.0%
u 818
 
4.2%
s 737
 
3.8%
i 686
 
3.5%
Other values (36) 7069
36.0%

Fed_electorate
Categorical

HIGH CORRELATION 

Distinct47
Distinct (%)2.1%
Missing8
Missing (%)0.4%
Memory size17.4 KiB
Parkes
 
116
Farrer
 
98
Riverina
 
96
Page
 
94
New England
 
93
Other values (42)
1711 

Length

Max length15
Median length11
Mean length7.3319746
Min length4

Characters and Unicode

Total characters16189
Distinct characters41
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReid
2nd rowNew England
3rd rowPaterson
4th rowEden-Monaro
5th rowNewcastle

Common Values

ValueCountFrequency (%)
Parkes 116
 
5.2%
Farrer 98
 
4.4%
Riverina 96
 
4.3%
Page 94
 
4.2%
New England 93
 
4.2%
Calare 79
 
3.6%
Lyne 71
 
3.2%
Eden-Monaro 67
 
3.0%
Hume 66
 
3.0%
Hunter 62
 
2.8%
Other values (37) 1366
61.6%

Length

2023-08-20T01:36:59.326129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
parkes 116
 
4.9%
farrer 98
 
4.2%
riverina 96
 
4.1%
page 94
 
4.0%
new 93
 
4.0%
england 93
 
4.0%
calare 79
 
3.4%
lyne 71
 
3.0%
eden-monaro 67
 
2.9%
hume 66
 
2.8%
Other values (39) 1477
62.9%

Most occurring characters

ValueCountFrequency (%)
a 1830
 
11.3%
e 1748
 
10.8%
r 1658
 
10.2%
n 1368
 
8.5%
o 812
 
5.0%
l 769
 
4.8%
i 730
 
4.5%
t 565
 
3.5%
d 534
 
3.3%
h 480
 
3.0%
Other values (31) 5695
35.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13528
83.6%
Uppercase Letter 2452
 
15.1%
Space Separator 142
 
0.9%
Dash Punctuation 67
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1830
13.5%
e 1748
12.9%
r 1658
12.3%
n 1368
10.1%
o 812
 
6.0%
l 769
 
5.7%
i 730
 
5.4%
t 565
 
4.2%
d 534
 
3.9%
h 480
 
3.5%
Other values (14) 3034
22.4%
Uppercase Letter
ValueCountFrequency (%)
M 292
11.9%
P 290
11.8%
C 264
10.8%
R 211
8.6%
B 201
8.2%
H 169
6.9%
N 161
 
6.6%
E 160
 
6.5%
W 157
 
6.4%
F 136
 
5.5%
Other values (5) 411
16.8%
Space Separator
ValueCountFrequency (%)
142
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 67
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15980
98.7%
Common 209
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1830
 
11.5%
e 1748
 
10.9%
r 1658
 
10.4%
n 1368
 
8.6%
o 812
 
5.1%
l 769
 
4.8%
i 730
 
4.6%
t 565
 
3.5%
d 534
 
3.3%
h 480
 
3.0%
Other values (29) 5486
34.3%
Common
ValueCountFrequency (%)
142
67.9%
- 67
32.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16189
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1830
 
11.3%
e 1748
 
10.8%
r 1658
 
10.2%
n 1368
 
8.5%
o 812
 
5.0%
l 769
 
4.8%
i 730
 
4.5%
t 565
 
3.5%
d 534
 
3.3%
h 480
 
3.0%
Other values (31) 5695
35.2%

Operational_directorate
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
Regional North
301 
Rural South and West
297 
Metropolitan South
279 
Rural North
269 
Metropolitan South and West
267 
Other values (5)
803 

Length

Max length27
Median length21
Mean length18.11417
Min length10

Characters and Unicode

Total characters40141
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMetropolitan South
2nd rowRegional North and West
3rd rowRegional North
4th rowRural South and West
5th rowRegional North

Common Values

ValueCountFrequency (%)
Regional North 301
13.6%
Rural South and West 297
13.4%
Metropolitan South 279
12.6%
Rural North 269
12.1%
Metropolitan South and West 267
12.0%
Metropolitan North 261
11.8%
Regional South 254
11.5%
Regional North and West 253
11.4%
Connected Communities 34
 
1.5%
Unassigned 1
 
< 0.1%

Length

2023-08-20T01:36:59.505519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-20T01:36:59.696816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
south 1097
18.1%
north 1084
17.9%
and 817
13.5%
west 817
13.5%
regional 808
13.3%
metropolitan 807
13.3%
rural 566
9.3%
connected 34
 
0.6%
communities 34
 
0.6%
unassigned 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
t 4680
11.7%
o 4671
11.6%
3849
 
9.6%
a 2999
 
7.5%
n 2536
 
6.3%
e 2535
 
6.3%
r 2457
 
6.1%
h 2181
 
5.4%
l 2181
 
5.4%
u 1697
 
4.2%
Other values (14) 10355
25.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31044
77.3%
Uppercase Letter 5248
 
13.1%
Space Separator 3849
 
9.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 4680
15.1%
o 4671
15.0%
a 2999
9.7%
n 2536
8.2%
e 2535
8.2%
r 2457
7.9%
h 2181
7.0%
l 2181
7.0%
u 1697
 
5.5%
i 1684
 
5.4%
Other values (6) 3423
11.0%
Uppercase Letter
ValueCountFrequency (%)
R 1374
26.2%
S 1097
20.9%
N 1084
20.7%
W 817
15.6%
M 807
15.4%
C 68
 
1.3%
U 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
3849
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 36292
90.4%
Common 3849
 
9.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 4680
12.9%
o 4671
12.9%
a 2999
 
8.3%
n 2536
 
7.0%
e 2535
 
7.0%
r 2457
 
6.8%
h 2181
 
6.0%
l 2181
 
6.0%
u 1697
 
4.7%
i 1684
 
4.6%
Other values (13) 8671
23.9%
Common
ValueCountFrequency (%)
3849
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40141
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 4680
11.7%
o 4671
11.6%
3849
 
9.6%
a 2999
 
7.5%
n 2536
 
6.3%
e 2535
 
6.3%
r 2457
 
6.1%
h 2181
 
5.4%
l 2181
 
5.4%
u 1697
 
4.2%
Other values (14) 10355
25.8%
Distinct114
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
2023-08-20T01:36:59.981496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length16
Mean length10.481498
Min length4

Characters and Unicode

Total characters23227
Distinct characters53
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowIron Cove
2nd rowUpper Hunter
3rd rowCessnock
4th rowEden-Monaro
5th rowGlenrock
ValueCountFrequency (%)
lake 101
 
3.0%
coast 101
 
3.0%
the 83
 
2.5%
north 78
 
2.3%
macquarie 61
 
1.8%
west 59
 
1.8%
port 58
 
1.7%
wagga 44
 
1.3%
hills 41
 
1.2%
river 41
 
1.2%
Other values (127) 2665
80.0%
2023-08-20T01:37:00.416877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2276
 
9.8%
e 2170
 
9.3%
r 1812
 
7.8%
o 1561
 
6.7%
n 1510
 
6.5%
t 1222
 
5.3%
l 1216
 
5.2%
1116
 
4.8%
i 1045
 
4.5%
s 908
 
3.9%
Other values (43) 8391
36.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18697
80.5%
Uppercase Letter 3339
 
14.4%
Space Separator 1116
 
4.8%
Dash Punctuation 41
 
0.2%
Decimal Number 34
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2276
12.2%
e 2170
11.6%
r 1812
9.7%
o 1561
 
8.3%
n 1510
 
8.1%
t 1222
 
6.5%
l 1216
 
6.5%
i 1045
 
5.6%
s 908
 
4.9%
u 756
 
4.0%
Other values (14) 4221
22.6%
Uppercase Letter
ValueCountFrequency (%)
C 489
14.6%
M 336
10.1%
W 300
9.0%
B 256
 
7.7%
L 223
 
6.7%
H 222
 
6.6%
T 212
 
6.3%
P 196
 
5.9%
G 196
 
5.9%
N 175
 
5.2%
Other values (14) 734
22.0%
Decimal Number
ValueCountFrequency (%)
1 12
35.3%
2 11
32.4%
3 11
32.4%
Space Separator
ValueCountFrequency (%)
1116
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22036
94.9%
Common 1191
 
5.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2276
 
10.3%
e 2170
 
9.8%
r 1812
 
8.2%
o 1561
 
7.1%
n 1510
 
6.9%
t 1222
 
5.5%
l 1216
 
5.5%
i 1045
 
4.7%
s 908
 
4.1%
u 756
 
3.4%
Other values (38) 7560
34.3%
Common
ValueCountFrequency (%)
1116
93.7%
- 41
 
3.4%
1 12
 
1.0%
2 11
 
0.9%
3 11
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23227
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2276
 
9.8%
e 2170
 
9.3%
r 1812
 
7.8%
o 1561
 
6.7%
n 1510
 
6.5%
t 1222
 
5.3%
l 1216
 
5.2%
1116
 
4.8%
i 1045
 
4.5%
s 908
 
3.9%
Other values (43) 8391
36.1%

Operational_directorate_office
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)1.6%
Missing1
Missing (%)< 0.1%
Memory size17.4 KiB
Nirimba
248 
St Peters
 
140
Glenfield
 
121
MacPark
 
100
Wagga Wagga
 
84
Other values (30)
1522 

Length

Max length14
Median length12
Mean length8.7693002
Min length5

Characters and Unicode

Total characters19424
Distinct characters40
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSt Peters
2nd rowMaitland
3rd rowMaitland
4th rowQueanbeyan
5th rowGateshead West

Common Values

ValueCountFrequency (%)
Nirimba 248
 
11.2%
St Peters 140
 
6.3%
Glenfield 121
 
5.5%
MacPark 100
 
4.5%
Wagga Wagga 84
 
3.8%
Maitland 82
 
3.7%
Tuggerah 81
 
3.7%
Gateshead West 80
 
3.6%
Arncliffe 79
 
3.6%
Warilla 78
 
3.5%
Other values (25) 1122
50.6%

Length

2023-08-20T01:37:00.615774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nirimba 248
 
9.1%
wagga 168
 
6.2%
st 140
 
5.1%
peters 140
 
5.1%
glenfield 121
 
4.4%
macpark 100
 
3.7%
maitland 82
 
3.0%
tuggerah 81
 
3.0%
gateshead 80
 
2.9%
west 80
 
2.9%
Other values (32) 1487
54.5%

Most occurring characters

ValueCountFrequency (%)
a 2427
 
12.5%
e 1765
 
9.1%
r 1499
 
7.7%
i 1241
 
6.4%
l 1186
 
6.1%
t 1012
 
5.2%
n 890
 
4.6%
o 836
 
4.3%
b 666
 
3.4%
g 653
 
3.4%
Other values (30) 7249
37.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16085
82.8%
Uppercase Letter 2827
 
14.6%
Space Separator 512
 
2.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2427
15.1%
e 1765
11.0%
r 1499
 
9.3%
i 1241
 
7.7%
l 1186
 
7.4%
t 1012
 
6.3%
n 890
 
5.5%
o 836
 
5.2%
b 666
 
4.1%
g 653
 
4.1%
Other values (13) 3910
24.3%
Uppercase Letter
ValueCountFrequency (%)
W 444
15.7%
M 358
12.7%
G 338
12.0%
N 303
10.7%
P 296
10.5%
B 164
 
5.8%
A 157
 
5.6%
S 140
 
5.0%
D 137
 
4.8%
T 135
 
4.8%
Other values (6) 355
12.6%
Space Separator
ValueCountFrequency (%)
512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18912
97.4%
Common 512
 
2.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2427
 
12.8%
e 1765
 
9.3%
r 1499
 
7.9%
i 1241
 
6.6%
l 1186
 
6.3%
t 1012
 
5.4%
n 890
 
4.7%
o 836
 
4.4%
b 666
 
3.5%
g 653
 
3.5%
Other values (29) 6737
35.6%
Common
ValueCountFrequency (%)
512
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2427
 
12.5%
e 1765
 
9.1%
r 1499
 
7.7%
i 1241
 
6.4%
l 1186
 
6.1%
t 1012
 
5.2%
n 890
 
4.6%
o 836
 
4.3%
b 666
 
3.4%
g 653
 
3.4%
Other values (30) 7249
37.3%

Operational_directorate_office_phone
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)1.6%
Missing1
Missing (%)< 0.1%
Memory size17.4 KiB
9208 7611
248 
9582 5800
160 
9203 9900
 
121
9886 7000
 
100
02 6937 3800
 
84
Other values (30)
1502 

Length

Max length12
Median length9
Mean length9.5774266
Min length9

Characters and Unicode

Total characters21214
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9582 5800
2nd row4931 3500
3rd row4931 3500
4th row02 6200 5000
5th row4088 3518

Common Values

ValueCountFrequency (%)
9208 7611 248
 
11.2%
9582 5800 160
 
7.2%
9203 9900 121
 
5.5%
9886 7000 100
 
4.5%
02 6937 3800 84
 
3.8%
4931 3500 82
 
3.7%
4348 9100 81
 
3.7%
4088 3518 80
 
3.6%
9582 2800 79
 
3.6%
4267 6100 78
 
3.5%
Other values (25) 1102
49.7%

Length

2023-08-20T01:37:00.788951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
02 377
 
7.8%
9208 248
 
5.1%
7611 248
 
5.1%
9582 239
 
4.9%
9900 176
 
3.6%
5800 160
 
3.3%
5000 154
 
3.2%
9203 121
 
2.5%
9886 100
 
2.1%
7000 100
 
2.1%
Other values (58) 2922
60.3%

Most occurring characters

ValueCountFrequency (%)
0 5120
24.1%
2630
12.4%
9 1955
 
9.2%
6 1930
 
9.1%
8 1870
 
8.8%
2 1633
 
7.7%
3 1484
 
7.0%
5 1290
 
6.1%
1 1164
 
5.5%
7 1054
 
5.0%
Other values (2) 1084
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18550
87.4%
Space Separator 2630
 
12.4%
Uppercase Letter 34
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5120
27.6%
9 1955
 
10.5%
6 1930
 
10.4%
8 1870
 
10.1%
2 1633
 
8.8%
3 1484
 
8.0%
5 1290
 
7.0%
1 1164
 
6.3%
7 1054
 
5.7%
4 1050
 
5.7%
Space Separator
ValueCountFrequency (%)
2630
100.0%
Uppercase Letter
ValueCountFrequency (%)
 34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21180
99.8%
Latin 34
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5120
24.2%
2630
12.4%
9 1955
 
9.2%
6 1930
 
9.1%
8 1870
 
8.8%
2 1633
 
7.7%
3 1484
 
7.0%
5 1290
 
6.1%
1 1164
 
5.5%
7 1054
 
5.0%
Latin
ValueCountFrequency (%)
 34
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21180
99.8%
None 34
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5120
24.2%
2630
12.4%
9 1955
 
9.2%
6 1930
 
9.1%
8 1870
 
8.8%
2 1633
 
7.7%
3 1484
 
7.0%
5 1290
 
6.1%
1 1164
 
5.5%
7 1054
 
5.0%
None
ValueCountFrequency (%)
 34
100.0%

Operational_directorate_office_address
Categorical

HIGH CORRELATION 

Distinct36
Distinct (%)1.6%
Missing1
Missing (%)< 0.1%
Memory size17.4 KiB
Building T3C, Nirimba Education Precinct, Eastern Rd, Quakers Hill 2763
248 
Church St, St Peters 2044
 
140
Roy Watts Rd, Glenfield 2167
 
121
Level 2, 75 Talavera Rd, Macquarie Park 2113
 
100
Level 4, 76 Morgan St, Wagga Wagga 2650
 
84
Other values (31)
1522 

Length

Max length71
Median length48
Mean length40.553499
Min length23

Characters and Unicode

Total characters89826
Distinct characters60
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChurch St, St Peters 2044
2nd rowLevel 1, 2 Caroline Pl, Maitland 2320
3rd rowLevel 1, 2 Caroline Pl, Maitland 2320
4th rowLevel 1, City Link Plaza, 24-36 Morisset St, Queanbeyan 2620
5th row40-44 Coral Cr, Gateshead West 2290

Common Values

ValueCountFrequency (%)
Building T3C, Nirimba Education Precinct, Eastern Rd, Quakers Hill 2763 248
 
11.2%
Church St, St Peters 2044 140
 
6.3%
Roy Watts Rd, Glenfield 2167 121
 
5.5%
Level 2, 75 Talavera Rd, Macquarie Park 2113 100
 
4.5%
Level 4, 76 Morgan St, Wagga Wagga 2650 84
 
3.8%
Level 1, 2 Caroline Pl, Maitland 2320 82
 
3.7%
14 Pioneer Ave, Tuggerah 2259 81
 
3.7%
40-44 Coral Cr, Gateshead West 2290 80
 
3.6%
Cnr Segenhoe & Avenal St, Arncliffe 2205 79
 
3.6%
30 Oldfield St, Warilla 2528 78
 
3.5%
Other values (26) 1122
50.6%

Length

2023-08-20T01:37:00.972656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
st 1219
 
7.9%
level 671
 
4.4%
rd 608
 
4.0%
2 415
 
2.7%
building 284
 
1.8%
hill 266
 
1.7%
2763 248
 
1.6%
t3c 248
 
1.6%
quakers 248
 
1.6%
education 248
 
1.6%
Other values (166) 10930
71.0%

Most occurring characters

ValueCountFrequency (%)
13170
 
14.7%
e 6163
 
6.9%
a 5117
 
5.7%
r 4141
 
4.6%
i 3945
 
4.4%
t 3863
 
4.3%
l 3857
 
4.3%
, 3686
 
4.1%
n 3626
 
4.0%
2 3394
 
3.8%
Other values (50) 38864
43.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 47871
53.3%
Decimal Number 13245
 
14.7%
Space Separator 13170
 
14.7%
Uppercase Letter 11251
 
12.5%
Other Punctuation 3860
 
4.3%
Dash Punctuation 347
 
0.4%
Open Punctuation 41
 
< 0.1%
Close Punctuation 41
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6163
12.9%
a 5117
10.7%
r 4141
 
8.7%
i 3945
 
8.2%
t 3863
 
8.1%
l 3857
 
8.1%
n 3626
 
7.6%
o 2355
 
4.9%
d 2044
 
4.3%
u 1858
 
3.9%
Other values (14) 10902
22.8%
Uppercase Letter
ValueCountFrequency (%)
S 1513
13.4%
C 1183
10.5%
R 906
 
8.1%
L 888
 
7.9%
P 877
 
7.8%
M 714
 
6.3%
B 692
 
6.2%
W 665
 
5.9%
E 496
 
4.4%
A 483
 
4.3%
Other values (10) 2834
25.2%
Decimal Number
ValueCountFrequency (%)
2 3394
25.6%
0 1917
14.5%
4 1403
10.6%
1 1363
10.3%
3 1197
 
9.0%
6 1118
 
8.4%
7 956
 
7.2%
5 953
 
7.2%
8 480
 
3.6%
9 464
 
3.5%
Other Punctuation
ValueCountFrequency (%)
, 3686
95.5%
& 174
 
4.5%
Space Separator
ValueCountFrequency (%)
13170
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 347
100.0%
Open Punctuation
ValueCountFrequency (%)
( 41
100.0%
Close Punctuation
ValueCountFrequency (%)
) 41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 59122
65.8%
Common 30704
34.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6163
 
10.4%
a 5117
 
8.7%
r 4141
 
7.0%
i 3945
 
6.7%
t 3863
 
6.5%
l 3857
 
6.5%
n 3626
 
6.1%
o 2355
 
4.0%
d 2044
 
3.5%
u 1858
 
3.1%
Other values (34) 22153
37.5%
Common
ValueCountFrequency (%)
13170
42.9%
, 3686
 
12.0%
2 3394
 
11.1%
0 1917
 
6.2%
4 1403
 
4.6%
1 1363
 
4.4%
3 1197
 
3.9%
6 1118
 
3.6%
7 956
 
3.1%
5 953
 
3.1%
Other values (6) 1547
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89826
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13170
 
14.7%
e 6163
 
6.9%
a 5117
 
5.7%
r 4141
 
4.6%
i 3945
 
4.4%
t 3863
 
4.3%
l 3857
 
4.3%
, 3686
 
4.1%
n 3626
 
4.0%
2 3394
 
3.8%
Other values (50) 38864
43.3%

FACS_district
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.4%
Missing10
Missing (%)0.5%
Memory size17.4 KiB
Hunter New England & Central Coast
454 
South Eastern Sydney, Northern Sydney & Sydney
423 
Murrumbidgee, Far West & Western NSW
355 
Western Sydney & Nepean Blue Mountains
304 
South Western Sydney
247 
Other values (3)
423 

Length

Max length46
Median length36
Mean length35.289211
Min length7

Characters and Unicode

Total characters77848
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSouth Eastern Sydney, Northern Sydney & Sydney
2nd rowHunter New England & Central Coast
3rd rowHunter New England & Central Coast
4th rowIllawarra Shoalhaven & Southern NSW
5th rowHunter New England & Central Coast

Common Values

ValueCountFrequency (%)
Hunter New England & Central Coast 454
20.5%
South Eastern Sydney, Northern Sydney & Sydney 423
19.1%
Murrumbidgee, Far West & Western NSW 355
16.0%
Western Sydney & Nepean Blue Mountains 304
13.7%
South Western Sydney 247
11.1%
Mid North Coast & Northern NSW 219
9.9%
Illawarra Shoalhaven & Southern NSW 203
9.2%
Unknown 1
 
< 0.1%
(Missing) 10
 
0.5%

Length

2023-08-20T01:37:01.152628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-20T01:37:01.335464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1958
15.4%
sydney 1820
14.3%
western 906
 
7.1%
nsw 777
 
6.1%
coast 673
 
5.3%
south 670
 
5.3%
northern 642
 
5.1%
hunter 454
 
3.6%
new 454
 
3.6%
england 454
 
3.6%
Other values (14) 3902
30.7%

Most occurring characters

ValueCountFrequency (%)
10504
13.5%
e 8442
 
10.8%
n 6928
 
8.9%
r 5414
 
7.0%
t 5303
 
6.8%
a 3982
 
5.1%
S 3673
 
4.7%
y 3640
 
4.7%
o 2915
 
3.7%
d 2848
 
3.7%
Other values (24) 24199
31.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 52302
67.2%
Uppercase Letter 12306
 
15.8%
Space Separator 10504
 
13.5%
Other Punctuation 2736
 
3.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8442
16.1%
n 6928
13.2%
r 5414
10.4%
t 5303
10.1%
a 3982
7.6%
y 3640
7.0%
o 2915
 
5.6%
d 2848
 
5.4%
s 2661
 
5.1%
u 2645
 
5.1%
Other values (10) 7524
14.4%
Uppercase Letter
ValueCountFrequency (%)
S 3673
29.8%
N 2396
19.5%
W 2038
16.6%
C 1127
 
9.2%
M 878
 
7.1%
E 877
 
7.1%
H 454
 
3.7%
F 355
 
2.9%
B 304
 
2.5%
I 203
 
1.6%
Other Punctuation
ValueCountFrequency (%)
& 1958
71.6%
, 778
 
28.4%
Space Separator
ValueCountFrequency (%)
10504
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 64608
83.0%
Common 13240
 
17.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8442
13.1%
n 6928
 
10.7%
r 5414
 
8.4%
t 5303
 
8.2%
a 3982
 
6.2%
S 3673
 
5.7%
y 3640
 
5.6%
o 2915
 
4.5%
d 2848
 
4.4%
s 2661
 
4.1%
Other values (21) 18802
29.1%
Common
ValueCountFrequency (%)
10504
79.3%
& 1958
 
14.8%
, 778
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10504
13.5%
e 8442
 
10.8%
n 6928
 
8.9%
r 5414
 
7.0%
t 5303
 
6.8%
a 3982
 
5.1%
S 3673
 
4.7%
y 3640
 
4.7%
o 2915
 
3.7%
d 2848
 
3.7%
Other values (24) 24199
31.1%

Local_health_district
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.7%
Missing10
Missing (%)0.5%
Memory size17.4 KiB
Hunter New England
377 
South Western Sydney
247 
Western Sydney
185 
Murrumbidgee
172 
Northern Sydney
159 
Other values (11)
1066 

Length

Max length21
Median length18
Mean length15.502267
Min length6

Characters and Unicode

Total characters34198
Distinct characters32
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSydney
2nd rowHunter New England
3rd rowHunter New England
4th rowSouthern NSW
5th rowHunter New England

Common Values

ValueCountFrequency (%)
Hunter New England 377
17.0%
South Western Sydney 247
11.1%
Western Sydney 185
8.3%
Murrumbidgee 172
7.8%
Northern Sydney 159
7.2%
Western NSW 157
7.1%
South Eastern Sydney 154
6.9%
Northern NSW 138
 
6.2%
Nepean Blue Mountains 119
 
5.4%
Illawarra Shoalhaven 118
 
5.3%
Other values (6) 380
17.1%

Length

2023-08-20T01:37:01.563281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sydney 855
16.7%
western 589
11.5%
south 401
 
7.9%
nsw 380
 
7.4%
hunter 377
 
7.4%
england 377
 
7.4%
new 377
 
7.4%
northern 297
 
5.8%
murrumbidgee 172
 
3.4%
coast 158
 
3.1%
Other values (13) 1124
22.0%

Most occurring characters

ValueCountFrequency (%)
e 4245
12.4%
n 3666
 
10.7%
2901
 
8.5%
r 2563
 
7.5%
t 2364
 
6.9%
S 1839
 
5.4%
y 1710
 
5.0%
a 1620
 
4.7%
d 1485
 
4.3%
u 1445
 
4.2%
Other values (22) 10360
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25430
74.4%
Uppercase Letter 5867
 
17.2%
Space Separator 2901
 
8.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4245
16.7%
n 3666
14.4%
r 2563
10.1%
t 2364
9.3%
y 1710
6.7%
a 1620
 
6.4%
d 1485
 
5.8%
u 1445
 
5.7%
o 1260
 
5.0%
h 1100
 
4.3%
Other values (10) 3972
15.6%
Uppercase Letter
ValueCountFrequency (%)
S 1839
31.3%
N 1254
21.4%
W 995
17.0%
E 531
 
9.1%
H 377
 
6.4%
M 372
 
6.3%
C 235
 
4.0%
B 119
 
2.0%
I 118
 
2.0%
F 26
 
0.4%
Space Separator
ValueCountFrequency (%)
2901
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 31297
91.5%
Common 2901
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4245
13.6%
n 3666
11.7%
r 2563
 
8.2%
t 2364
 
7.6%
S 1839
 
5.9%
y 1710
 
5.5%
a 1620
 
5.2%
d 1485
 
4.7%
u 1445
 
4.6%
o 1260
 
4.0%
Other values (21) 9100
29.1%
Common
ValueCountFrequency (%)
2901
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34198
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 4245
12.4%
n 3666
 
10.7%
2901
 
8.5%
r 2563
 
7.5%
t 2364
 
6.9%
S 1839
 
5.4%
y 1710
 
5.0%
a 1620
 
4.7%
d 1485
 
4.3%
u 1445
 
4.2%
Other values (22) 10360
30.3%

AECG_region
Categorical

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)0.9%
Missing24
Missing (%)1.1%
Memory size17.4 KiB
Metropolitan West
249 
Metropolitan East
228 
Metropolitan South West
222 
Hunter
212 
Upper South Coast
180 
Other values (15)
1101 

Length

Max length23
Median length18
Mean length14.739507
Min length6

Characters and Unicode

Total characters32309
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMetropolitan East
2nd rowHunter
3rd rowHunter
4th rowLower South Coast
5th rowHunter

Common Values

ValueCountFrequency (%)
Metropolitan West 249
11.2%
Metropolitan East 228
10.3%
Metropolitan South West 222
10.0%
Hunter 212
9.6%
Upper South Coast 180
8.1%
Metropolitan North 174
 
7.9%
Upper North Coast 138
 
6.2%
Riverina 1 136
 
6.1%
Western 1 125
 
5.6%
North Western 2 79
 
3.6%
Other values (10) 449
20.3%

Length

2023-08-20T01:37:01.747654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
metropolitan 873
17.9%
north 543
11.1%
coast 513
10.5%
west 471
9.7%
south 441
9.0%
1 335
 
6.9%
upper 318
 
6.5%
western 311
 
6.4%
east 228
 
4.7%
hunter 212
 
4.4%
Other values (7) 628
12.9%

Most occurring characters

ValueCountFrequency (%)
t 4543
14.1%
o 3360
10.4%
e 2865
 
8.9%
r 2687
 
8.3%
2681
 
8.3%
a 2097
 
6.5%
n 1792
 
5.5%
s 1523
 
4.7%
p 1509
 
4.7%
i 1269
 
3.9%
Other values (20) 7983
24.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24755
76.6%
Uppercase Letter 4388
 
13.6%
Space Separator 2681
 
8.3%
Decimal Number 485
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 4543
18.4%
o 3360
13.6%
e 2865
11.6%
r 2687
10.9%
a 2097
8.5%
n 1792
 
7.2%
s 1523
 
6.2%
p 1509
 
6.1%
i 1269
 
5.1%
h 1045
 
4.2%
Other values (5) 2065
8.3%
Uppercase Letter
ValueCountFrequency (%)
M 921
21.0%
W 782
17.8%
C 591
13.5%
N 543
12.4%
S 441
10.1%
U 318
 
7.2%
E 228
 
5.2%
H 212
 
4.8%
R 174
 
4.0%
L 117
 
2.7%
Decimal Number
ValueCountFrequency (%)
1 335
69.1%
2 124
 
25.6%
3 26
 
5.4%
Space Separator
ValueCountFrequency (%)
2681
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 29143
90.2%
Common 3166
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 4543
15.6%
o 3360
11.5%
e 2865
9.8%
r 2687
9.2%
a 2097
 
7.2%
n 1792
 
6.1%
s 1523
 
5.2%
p 1509
 
5.2%
i 1269
 
4.4%
h 1045
 
3.6%
Other values (16) 6453
22.1%
Common
ValueCountFrequency (%)
2681
84.7%
1 335
 
10.6%
2 124
 
3.9%
3 26
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32309
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 4543
14.1%
o 3360
10.4%
e 2865
 
8.9%
r 2687
 
8.3%
2681
 
8.3%
a 2097
 
6.5%
n 1792
 
5.5%
s 1523
 
4.7%
p 1509
 
4.7%
i 1269
 
3.9%
Other values (20) 7983
24.7%

ASGS_remoteness
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
Major Cities of Australia
1241 
Inner Regional Australia
582 
Outer Regional Australia
341 
Remote Australia
 
38
Very Remote Australia
 
12

Length

Max length25
Median length25
Mean length24.394856
Min length11

Characters and Unicode

Total characters54059
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMajor Cities of Australia
2nd rowInner Regional Australia
3rd rowMajor Cities of Australia
4th rowOuter Regional Australia
5th rowMajor Cities of Australia

Common Values

ValueCountFrequency (%)
Major Cities of Australia 1241
56.0%
Inner Regional Australia 582
26.3%
Outer Regional Australia 341
 
15.4%
Remote Australia 38
 
1.7%
Very Remote Australia 12
 
0.5%
Not Defined 2
 
0.1%

Length

2023-08-20T01:37:01.918712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-20T01:37:02.080396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
australia 2214
28.2%
major 1241
15.8%
cities 1241
15.8%
of 1241
15.8%
regional 923
11.8%
inner 582
 
7.4%
outer 341
 
4.3%
remote 50
 
0.6%
very 12
 
0.2%
not 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 6592
12.2%
5633
10.4%
i 5621
10.4%
r 4390
 
8.1%
t 3848
 
7.1%
o 3457
 
6.4%
s 3455
 
6.4%
e 3203
 
5.9%
l 3137
 
5.8%
u 2555
 
4.7%
Other values (16) 12168
22.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 41818
77.4%
Uppercase Letter 6608
 
12.2%
Space Separator 5633
 
10.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6592
15.8%
i 5621
13.4%
r 4390
10.5%
t 3848
9.2%
o 3457
8.3%
s 3455
8.3%
e 3203
7.7%
l 3137
7.5%
u 2555
 
6.1%
n 2089
 
5.0%
Other values (6) 3471
8.3%
Uppercase Letter
ValueCountFrequency (%)
A 2214
33.5%
M 1241
18.8%
C 1241
18.8%
R 973
14.7%
I 582
 
8.8%
O 341
 
5.2%
V 12
 
0.2%
N 2
 
< 0.1%
D 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
5633
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 48426
89.6%
Common 5633
 
10.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6592
13.6%
i 5621
11.6%
r 4390
9.1%
t 3848
 
7.9%
o 3457
 
7.1%
s 3455
 
7.1%
e 3203
 
6.6%
l 3137
 
6.5%
u 2555
 
5.3%
A 2214
 
4.6%
Other values (15) 9954
20.6%
Common
ValueCountFrequency (%)
5633
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54059
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6592
12.2%
5633
10.4%
i 5621
10.4%
r 4390
 
8.1%
t 3848
 
7.1%
o 3457
 
6.4%
s 3455
 
6.4%
e 3203
 
5.9%
l 3137
 
5.8%
u 2555
 
4.7%
Other values (16) 12168
22.5%

Latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct2214
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-33.185415
Minimum-37.084209
Maximum-28.16951
Zeros0
Zeros (%)0.0%
Negative2216
Negative (%)100.0%
Memory size17.4 KiB
2023-08-20T01:37:02.277389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-37.084209
5-th percentile-35.355771
Q1-33.979356
median-33.766117
Q3-32.810491
95-th percentile-28.928409
Maximum-28.16951
Range8.914699
Interquartile range (IQR)1.1688652

Descriptive statistics

Standard deviation1.7334563
Coefficient of variation (CV)-0.052235486
Kurtosis1.1177057
Mean-33.185415
Median Absolute Deviation (MAD)0.5007065
Skewness1.1719333
Sum-73538.879
Variance3.0048706
MonotonicityNot monotonic
2023-08-20T01:37:02.488700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-32.908099 2
 
0.1%
-31.10651 2
 
0.1%
-29.691603 1
 
< 0.1%
-32.2525 1
 
< 0.1%
-33.870879 1
 
< 0.1%
-31.892305 1
 
< 0.1%
-33.924753 1
 
< 0.1%
-33.866464 1
 
< 0.1%
-32.606539 1
 
< 0.1%
-30.153584 1
 
< 0.1%
Other values (2204) 2204
99.5%
ValueCountFrequency (%)
-37.084209 1
< 0.1%
-37.063271 1
< 0.1%
-37.055009 1
< 0.1%
-37.041739 1
< 0.1%
-36.92962 1
< 0.1%
-36.925482 1
< 0.1%
-36.918986 1
< 0.1%
-36.917647 1
< 0.1%
-36.886525 1
< 0.1%
-36.832664 1
< 0.1%
ValueCountFrequency (%)
-28.16951 1
< 0.1%
-28.195346 1
< 0.1%
-28.195651 1
< 0.1%
-28.196814 1
< 0.1%
-28.207696 1
< 0.1%
-28.215457 1
< 0.1%
-28.215705 1
< 0.1%
-28.222422 1
< 0.1%
-28.223742 1
< 0.1%
-28.229959 1
< 0.1%

Longitude
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct2216
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.62069
Minimum141.43997
Maximum159.06903
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2023-08-20T01:37:02.671011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum141.43997
5-th percentile146.90061
Q1150.58974
median150.99211
Q3151.31204
95-th percentile153.11617
Maximum159.06903
Range17.629062
Interquartile range (IQR)0.72229675

Descriptive statistics

Standard deviation1.8374202
Coefficient of variation (CV)0.012198989
Kurtosis5.3435874
Mean150.62069
Median Absolute Deviation (MAD)0.358081
Skewness-1.7938336
Sum333775.46
Variance3.376113
MonotonicityNot monotonic
2023-08-20T01:37:02.866282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
151.131206 1
 
< 0.1%
149.583789 1
 
< 0.1%
151.436848 1
 
< 0.1%
148.627132 1
 
< 0.1%
151.22024 1
 
< 0.1%
152.459892 1
 
< 0.1%
151.068761 1
 
< 0.1%
150.890898 1
 
< 0.1%
152.110359 1
 
< 0.1%
151.875219 1
 
< 0.1%
Other values (2206) 2206
99.5%
ValueCountFrequency (%)
141.43997 1
< 0.1%
141.441054 1
< 0.1%
141.456502 1
< 0.1%
141.456986 1
< 0.1%
141.460807 1
< 0.1%
141.462443 1
< 0.1%
141.471518 1
< 0.1%
141.472 1
< 0.1%
141.473022 1
< 0.1%
141.89493 1
< 0.1%
ValueCountFrequency (%)
159.069032 1
< 0.1%
153.616057 1
< 0.1%
153.614826 1
< 0.1%
153.590695 1
< 0.1%
153.585664 1
< 0.1%
153.5846 1
< 0.1%
153.579497 1
< 0.1%
153.572189 1
< 0.1%
153.570474 1
< 0.1%
153.567798 1
< 0.1%

Assets unit
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.4%
Missing38
Missing (%)1.7%
Memory size17.4 KiB
Southern NSW
395 
North Western NSW
304 
Hunter/Central Coast
290 
South Western Sydney
279 
North Coast
263 
Other values (3)
647 

Length

Max length20
Median length15
Mean length14.502296
Min length6

Characters and Unicode

Total characters31586
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSydney
2nd rowHunter/Central Coast
3rd rowHunter/Central Coast
4th rowSouthern NSW
5th rowHunter/Central Coast

Common Values

ValueCountFrequency (%)
Southern NSW 395
17.8%
North Western NSW 304
13.7%
Hunter/Central Coast 290
13.1%
South Western Sydney 279
12.6%
North Coast 263
11.9%
Western Sydney 248
11.2%
Sydney 228
10.3%
Northern Sydney 171
7.7%
(Missing) 38
 
1.7%

Length

2023-08-20T01:37:03.063672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-20T01:37:03.243537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
sydney 926
19.7%
western 831
17.6%
nsw 699
14.8%
north 567
12.0%
coast 553
11.7%
southern 395
8.4%
hunter/central 290
 
6.2%
south 279
 
5.9%
northern 171
 
3.6%

Most occurring characters

ValueCountFrequency (%)
e 3734
11.8%
t 3376
10.7%
n 2903
9.2%
r 2715
 
8.6%
2533
 
8.0%
S 2299
 
7.3%
o 1965
 
6.2%
y 1852
 
5.9%
W 1530
 
4.8%
N 1437
 
4.5%
Other values (9) 7242
22.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22364
70.8%
Uppercase Letter 6399
 
20.3%
Space Separator 2533
 
8.0%
Other Punctuation 290
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3734
16.7%
t 3376
15.1%
n 2903
13.0%
r 2715
12.1%
o 1965
8.8%
y 1852
8.3%
h 1412
 
6.3%
s 1384
 
6.2%
u 964
 
4.3%
d 926
 
4.1%
Other values (2) 1133
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
S 2299
35.9%
W 1530
23.9%
N 1437
22.5%
C 843
 
13.2%
H 290
 
4.5%
Space Separator
ValueCountFrequency (%)
2533
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 290
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 28763
91.1%
Common 2823
 
8.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3734
13.0%
t 3376
11.7%
n 2903
10.1%
r 2715
9.4%
S 2299
8.0%
o 1965
 
6.8%
y 1852
 
6.4%
W 1530
 
5.3%
N 1437
 
5.0%
h 1412
 
4.9%
Other values (7) 5540
19.3%
Common
ValueCountFrequency (%)
2533
89.7%
/ 290
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31586
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3734
11.8%
t 3376
10.7%
n 2903
9.2%
r 2715
 
8.6%
2533
 
8.0%
S 2299
 
7.3%
o 1965
 
6.2%
y 1852
 
5.9%
W 1530
 
4.8%
N 1437
 
4.5%
Other values (9) 7242
22.9%

SA4
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)1.3%
Missing1
Missing (%)< 0.1%
Memory size17.4 KiB
New England and North West
 
121
Newcastle and Lake Macquarie
 
119
Central West
 
111
Sydney - Inner South West
 
111
Richmond - Tweed
 
111
Other values (23)
1642 

Length

Max length38
Median length25
Mean length20.767946
Min length6

Characters and Unicode

Total characters46001
Distinct characters43
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSydney - Inner West
2nd rowHunter Valley exc Newcastle
3rd rowHunter Valley exc Newcastle
4th rowCapital Region
5th rowNewcastle and Lake Macquarie

Common Values

ValueCountFrequency (%)
New England and North West 121
 
5.5%
Newcastle and Lake Macquarie 119
 
5.4%
Central West 111
 
5.0%
Sydney - Inner South West 111
 
5.0%
Richmond - Tweed 111
 
5.0%
Capital Region 104
 
4.7%
Hunter Valley exc Newcastle 96
 
4.3%
Riverina 94
 
4.2%
Sydney - Outer West and Blue Mountains 91
 
4.1%
Sydney - South West 90
 
4.1%
Other values (18) 1167
52.7%

Length

2023-08-20T01:37:03.456511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1121
 
14.0%
sydney 1017
 
12.7%
west 737
 
9.2%
and 635
 
7.9%
south 334
 
4.2%
north 278
 
3.5%
newcastle 215
 
2.7%
inner 210
 
2.6%
central 188
 
2.3%
outer 175
 
2.2%
Other values (39) 3092
38.6%

Most occurring characters

ValueCountFrequency (%)
5787
 
12.6%
e 4341
 
9.4%
a 3757
 
8.2%
n 3671
 
8.0%
t 2942
 
6.4%
y 2408
 
5.2%
r 2388
 
5.2%
d 2228
 
4.8%
s 1617
 
3.5%
o 1566
 
3.4%
Other values (33) 15296
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 32943
71.6%
Uppercase Letter 6150
 
13.4%
Space Separator 5787
 
12.6%
Dash Punctuation 1121
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4341
13.2%
a 3757
11.4%
n 3671
11.1%
t 2942
8.9%
y 2408
 
7.3%
r 2388
 
7.2%
d 2228
 
6.8%
s 1617
 
4.9%
o 1566
 
4.8%
l 1504
 
4.6%
Other values (14) 6521
19.8%
Uppercase Letter
ValueCountFrequency (%)
S 1544
25.1%
W 737
12.0%
N 661
10.7%
C 568
 
9.2%
H 392
 
6.4%
M 366
 
6.0%
R 343
 
5.6%
I 298
 
4.8%
B 279
 
4.5%
O 254
 
4.1%
Other values (7) 708
11.5%
Space Separator
ValueCountFrequency (%)
5787
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1121
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 39093
85.0%
Common 6908
 
15.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4341
 
11.1%
a 3757
 
9.6%
n 3671
 
9.4%
t 2942
 
7.5%
y 2408
 
6.2%
r 2388
 
6.1%
d 2228
 
5.7%
s 1617
 
4.1%
o 1566
 
4.0%
S 1544
 
3.9%
Other values (31) 12631
32.3%
Common
ValueCountFrequency (%)
5787
83.8%
- 1121
 
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5787
 
12.6%
e 4341
 
9.4%
a 3757
 
8.2%
n 3671
 
8.0%
t 2942
 
6.4%
y 2408
 
5.2%
r 2388
 
5.2%
d 2228
 
4.8%
s 1617
 
3.5%
o 1566
 
3.4%
Other values (33) 15296
33.3%

Date_extracted
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
Minimum2023-08-18 00:00:00
Maximum2023-08-18 00:00:00
2023-08-20T01:37:03.558882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:37:03.648717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-08-20T01:36:43.804259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:38.350982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:39.310038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:40.197566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:41.088546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:41.996708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:42.930678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:43.950242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:38.501594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:39.446897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:40.337594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:41.231512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:42.140508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:43.068875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:44.070460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:38.626833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:39.555500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:40.454634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:41.374663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:42.285902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:43.186181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:44.199536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:38.761771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:39.677933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:40.578861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:41.497459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:42.413970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:43.310637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:44.325586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:38.890739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:39.810905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:40.699738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:41.613908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:42.539781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:43.431144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:44.460074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:39.030951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:39.952224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:40.832215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:41.743418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:42.670720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:43.559075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:44.587910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:39.160442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:40.068887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:40.954940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:41.865068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:42.795309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-20T01:36:43.676015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-08-20T01:37:03.749485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
School_codeAgeIDPostcodelatest_year_enrolment_FTEICSEA_valueLatitudeLongitudeLevel_of_schoolingSelective_schoolOpportunity_classSchool_specialty_typeSchool_subtypePreschool_indDistance_educationIntensive_english_centreSchool_genderLate_opening_schoolFed_electorateOperational_directorateOperational_directorate_officeOperational_directorate_office_phoneOperational_directorate_office_addressFACS_districtLocal_health_districtAECG_regionASGS_remotenessAssets unitSA4
School_code1.0000.015-0.1360.4660.020-0.1100.0160.5540.2070.0890.1170.4580.1110.0450.1740.1940.0800.1590.1320.1520.1540.1530.1230.1220.1270.1650.1370.154
AgeID0.0151.0000.501-0.248-0.217-0.039-0.2920.3370.0990.0000.3120.4700.0650.1250.1280.0890.0870.1750.1170.1440.1470.1490.1310.1450.1220.1390.1240.163
Postcode-0.1360.5011.000-0.333-0.468-0.007-0.5700.0660.0750.0460.0000.0580.0720.0000.0920.1330.2360.6920.5480.6850.6890.6880.6150.6870.6880.2800.6190.714
latest_year_enrolment_FTE0.466-0.248-0.3331.0000.355-0.1460.1320.3210.2730.1500.1220.2120.0880.0000.2030.1360.1900.1970.1620.1540.1510.1520.1690.1570.1520.2450.1760.180
ICSEA_value0.020-0.217-0.4680.3551.000-0.2100.2340.1280.3210.1300.0000.1090.1340.0000.0000.0690.3670.3510.2830.3060.3160.3150.2880.2750.2940.3240.3090.320
Latitude-0.110-0.039-0.007-0.146-0.2101.0000.5420.0580.0260.0290.0340.0320.0500.0260.0390.0760.3170.6750.5230.7700.7830.7830.5330.5940.7090.3950.5870.698
Longitude0.016-0.292-0.5700.1320.2340.5421.0000.0940.0210.0420.0000.0610.0730.0330.0250.0440.6190.6230.4260.7370.7420.7410.4980.6690.7130.4150.4830.605
Level_of_schooling0.5540.3370.0660.3210.1280.0580.0941.0000.2090.0960.1850.9550.1560.0790.1730.2040.2380.1150.1060.1220.1280.1270.3030.3030.0930.1510.1000.116
Selective_school0.2070.0990.0750.2730.3210.0260.0210.2091.0000.0080.3210.2070.0120.0000.0280.2080.0180.1270.0760.0730.0670.0710.0620.0780.0390.0480.0760.087
Opportunity_class0.0890.0000.0460.1500.1300.0290.0420.0960.0081.0000.0870.0650.0000.0000.0000.0000.0000.0090.0680.0000.0000.0000.0580.0430.0340.0680.0720.000
School_specialty_type0.1170.3120.0000.1220.0000.0340.0000.1850.3210.0871.0000.4650.0000.4100.2540.1090.0000.0720.0860.0000.0000.0000.1410.1020.0000.0000.0370.041
School_subtype0.4580.4700.0580.2120.1090.0320.0610.9550.2070.0650.4651.0000.1570.0550.3120.2310.2400.1000.1360.0630.0670.0670.0820.0560.0430.1380.0980.078
Preschool_ind0.1110.0650.0720.0880.1340.0500.0730.1560.0120.0000.0000.1571.0000.0450.0000.0120.0330.2050.1340.1420.1520.1510.0810.1380.1450.1080.1210.188
Distance_education0.0450.1250.0000.0000.0000.0260.0330.0790.0000.0000.4100.0550.0451.0000.0000.0000.0380.0750.0000.0640.0620.0600.0000.0510.0800.0000.0480.100
Intensive_english_centre0.1740.1280.0920.2030.0000.0390.0250.1730.0280.0000.2540.3120.0000.0001.0000.0310.0000.0980.0820.0110.0000.0000.0730.0510.0860.0450.1000.098
School_gender0.1940.0890.1330.1360.0690.0760.0440.2040.2080.0000.1090.2310.0120.0000.0311.0000.0180.1590.1340.1390.1380.1370.1220.1100.1050.0700.1180.164
Late_opening_school0.0800.0870.2360.1900.3670.3170.6190.2380.0180.0000.0000.2400.0330.0380.0000.0181.0000.7110.4130.6920.7090.7090.3880.5440.7920.6950.4540.597
Fed_electorate0.1590.1750.6920.1970.3510.6750.6230.1150.1270.0090.0720.1000.2050.0750.0980.1590.7111.0000.7710.6740.6680.6660.8470.8320.7690.5040.9290.820
Operational_directorate0.1320.1170.5480.1620.2830.5230.4260.1060.0760.0680.0860.1360.1340.0000.0820.1340.4130.7711.0000.8900.9180.9180.6020.6570.7070.3980.6490.748
Operational_directorate_office0.1520.1440.6850.1540.3060.7700.7370.1220.0730.0000.0000.0630.1420.0640.0110.1390.6920.6740.8901.0000.9851.0000.8390.8090.7900.4800.9100.748
Operational_directorate_office_phone0.1540.1470.6890.1510.3160.7830.7420.1280.0670.0000.0000.0670.1520.0620.0000.1380.7090.6680.9180.9851.0001.0000.8380.8110.7980.4850.9100.746
Operational_directorate_office_address0.1530.1490.6880.1520.3150.7830.7410.1270.0710.0000.0000.0670.1510.0600.0000.1370.7090.6660.9181.0001.0001.0000.8430.8150.8000.4850.9130.751
FACS_district0.1230.1310.6150.1690.2880.5330.4980.3030.0620.0580.1410.0820.0810.0000.0730.1220.3880.8470.6020.8390.8380.8431.0000.9980.8580.4000.7620.933
Local_health_district0.1220.1450.6870.1570.2750.5940.6690.3030.0780.0430.1020.0560.1380.0510.0510.1100.5440.8320.6570.8090.8110.8150.9981.0000.8130.4700.8890.876
AECG_region0.1270.1220.6880.1520.2940.7090.7130.0930.0390.0340.0000.0430.1450.0800.0860.1050.7920.7690.7070.7900.7980.8000.8580.8131.0000.6210.9890.766
ASGS_remoteness0.1650.1390.2800.2450.3240.3950.4150.1510.0480.0680.0000.1380.1080.0000.0450.0700.6950.5040.3980.4800.4850.4850.4000.4700.6211.0000.4540.502
Assets unit0.1370.1240.6190.1760.3090.5870.4830.1000.0760.0720.0370.0980.1210.0480.1000.1180.4540.9290.6490.9100.9100.9130.7620.8890.9890.4541.0000.939
SA40.1540.1630.7140.1800.3200.6980.6050.1160.0870.0000.0410.0780.1880.1000.0980.1640.5970.8200.7480.7480.7460.7510.9330.8760.7660.5020.9391.000

Missing values

2023-08-20T01:36:44.892673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-20T01:36:45.566452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-20T01:36:46.188297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

School_codeAgeIDSchool_nameStreetTown_suburbPostcodePhoneSchool_EmailWebsiteFaxlatest_year_enrolment_FTEIndigenous_pctLBOTE_pctICSEA_valueLevel_of_schoolingSelective_schoolOpportunity_classSchool_specialty_typeSchool_subtypeSupport_classesPreschool_indDistance_educationIntensive_english_centreSchool_genderLate_opening_schoolDate_1st_teacherLGAelectorate_from_2023electorate_2015_2022Fed_electorateOperational_directoratePrincipal_networkOperational_directorate_officeOperational_directorate_office_phoneOperational_directorate_office_addressFACS_districtLocal_health_districtAECG_regionASGS_remotenessLatitudeLongitudeAssets unitSA4Date_extracted
0100144402.0Abbotsford Public School350 Great North RdAbbotsford2046.09713 6220abbotsford-p.school@det.nsw.edu.auhttps://abbotsford-p.schools.nsw.gov.au9712 1825474.02.041.01111.0Primary SchoolNot SelectiveNComprehensiveKinder to Year 6NaNNNNCoedN1925-04-01Canada Bay (A)DrummoyneDrummoyneReidMetropolitan SouthIron CoveSt Peters9582 5800Church St, St Peters 2044South Eastern Sydney, Northern Sydney & SydneySydneyMetropolitan EastMajor Cities of Australia-33.852728151.131206SydneySydney - Inner West2023-08-18
1100249333.0Aberdeen Public SchoolSegenhoe StAberdeen2336.06543 7271aberdeen-p.school@det.nsw.edu.auhttps://aberdeen-p.schools.nsw.gov.au6543 7712166.022.0np895.0Primary SchoolNot SelectiveNComprehensiveKinder to Year 6NaNNNNCoedN1864-02-01Upper Hunter Shire (A)Upper HunterUpper HunterNew EnglandRegional North and WestUpper HunterMaitland4931 3500Level 1, 2 Caroline Pl, Maitland 2320Hunter New England & Central CoastHunter New EnglandHunterInner Regional Australia-32.166098150.888095Hunter/Central CoastHunter Valley exc Newcastle2023-08-18
2100349299.0Abermain Public SchoolGoulburn StAbermain2326.04930 4210abermain-p.school@det.nsw.edu.auhttps://abermain-p.schools.nsw.gov.au4930 4319265.026.0np893.0Primary SchoolNot SelectiveNComprehensiveKinder to Year 6NaNNNNCoedN1905-08-01Cessnock (C)CessnockCessnockPatersonRegional NorthCessnockMaitland4931 3500Level 1, 2 Caroline Pl, Maitland 2320Hunter New England & Central CoastHunter New EnglandHunterMajor Cities of Australia-32.808920151.426499Hunter/Central CoastHunter Valley exc Newcastle2023-08-18
3100750425.0Adaminaby Public School9 Cosgrove StreetADAMINABY2629.06454 2265adaminaby-p.school@det.nsw.edu.auhttps://adaminaby-p.schools.nsw.gov.au6454 255212.00.0np976.0Primary SchoolNot SelectiveNComprehensiveKinder to Year 6NaNNNNCoedN1869-01-01Snowy Monaro Regional (A)MonaroMonaroEden-MonaroRural South and WestEden-MonaroQueanbeyan02 6200 5000Level 1, City Link Plaza, 24-36 Morisset St, Queanbeyan 2620Illawarra Shoalhaven & Southern NSWSouthern NSWLower South CoastOuter Regional Australia-35.993292148.776721Southern NSWCapital Region2023-08-18
4100849043.0Adamstown Public SchoolBryant StAdamstown2289.04957 1114adamstown-p.school@det.nsw.edu.auhttps://adamstown-p.schools.nsw.gov.au4956 2446361.08.014.01060.0Primary SchoolNot SelectiveNComprehensiveKinder to Year 6NaNNNNCoedN1877-07-01Newcastle (C)NewcastleNewcastleNewcastleRegional NorthGlenrockGateshead West4088 351840-44 Coral Cr, Gateshead West 2290Hunter New England & Central CoastHunter New EnglandHunterMajor Cities of Australia-32.932213151.730971Hunter/Central CoastNewcastle and Lake Macquarie2023-08-18
5100950667.0Adelong Public School50 Gilmore StreetADELONG2729.06946 2053adelong-p.school@det.nsw.edu.auhttps://adelong-p.schools.nsw.gov.au6946 219947.0npnp962.0Primary SchoolNot SelectiveNComprehensiveKinder to Year 6NaNNNNCoedN1860-03-01Snowy Valleys (A)Wagga WaggaWagga WaggaEden-MonaroRural South and WestGundagaiWagga Wagga02 6937 3800Level 4, 76 Morgan St, Wagga Wagga 2650Murrumbidgee, Far West & Western NSWMurrumbidgeeRiverina 1Inner Regional Australia-35.312333148.062802Southern NSWRiverina2023-08-18
6101550020.0Albion Park Public SchoolTongarra & Hamilton RdsAlbion Park2527.04256 1244albionpk-p.school@det.nsw.edu.auhttps://albionpk-p.schools.nsw.gov.au4256 4160467.09.07.0968.0Primary SchoolNot SelectiveNComprehensiveKinder to Year 6NaNNNNCoedN1872-07-01Shellharbour (C)KiamaKiamaWhitlamRegional SouthLake Illawarra SouthWarilla4267 610030 Oldfield St, Warilla 2528Illawarra Shoalhaven & Southern NSWIllawarra ShoalhavenUpper South CoastMajor Cities of Australia-34.570257150.772620Southern NSWIllawarra2023-08-18
7101649357.0Timbumburi Public School542 Kia Ora LaneTimbumburi2340.06767 0232timbumburi-p.school@det.nsw.edu.auhttps://timbumburi-p.schools.nsw.gov.au6767 0245154.010.0np1012.0Primary SchoolNot SelectiveNComprehensiveKinder to Year 6NaNNNNCoedN1911-02-01Tamworth Regional (A)TamworthTamworthNew EnglandRural NorthPeelTamworth6755 5000Level 3, Noel Park House, 155-157 Marius St, Tamworth 2340Hunter New England & Central CoastHunter New EnglandNorth Western 2Outer Regional Australia-31.203781150.915629North Western NSWNew England and North West2023-08-18
8101750441.0Albury Public School481 David StAlbury2640.002 6021 3849albury-p.school@det.nsw.edu.auhttps://albury-p.schools.nsw.gov.au02 6041 1265607.05.010.01053.0Primary SchoolNot SelectiveNComprehensiveKinder to Year 6NaNNNNCoedN1850-07-01Albury (C)AlburyAlburyFarrerRural South and WestAlburyAlbury02 6051 4300521 Macauley St, Albury 2640Murrumbidgee, Far West & Western NSWMurrumbidgeeRiverina 1Inner Regional Australia-36.082454146.919253Southern NSWMurray2023-08-18
9101950442.0Albury West Public SchoolMott StAlbury2640.06021 2288alburywest-p.school@det.nsw.edu.auhttps://alburywest-p.schools.nsw.gov.au6041 3783166.023.08.0906.0Primary SchoolNot SelectiveNComprehensiveKinder to Year 6NaNNNNCoedN1936-01-01Albury (C)AlburyAlburyFarrerRural South and WestAlburyAlbury02 6051 4300521 Macauley St, Albury 2640Murrumbidgee, Far West & Western NSWMurrumbidgeeRiverina 1Inner Regional Australia-36.077830146.895548Southern NSWMurray2023-08-18
School_codeAgeIDSchool_nameStreetTown_suburbPostcodePhoneSchool_EmailWebsiteFaxlatest_year_enrolment_FTEIndigenous_pctLBOTE_pctICSEA_valueLevel_of_schoolingSelective_schoolOpportunity_classSchool_specialty_typeSchool_subtypeSupport_classesPreschool_indDistance_educationIntensive_english_centreSchool_genderLate_opening_schoolDate_1st_teacherLGAelectorate_from_2023electorate_2015_2022Fed_electorateOperational_directoratePrincipal_networkOperational_directorate_officeOperational_directorate_office_phoneOperational_directorate_office_addressFACS_districtLocal_health_districtAECG_regionASGS_remotenessLatitudeLongitudeAssets unitSA4Date_extracted
2206891170233.0The Ponds High School180 Riverbank DriveThe Ponds2769.09626 3562theponds-h.school@det.nsw.edu.auhttps://theponds-h.schools.nsw.gov.au9837 08231948.01.068.01093.0Secondary SchoolNot SelectiveNComprehensiveYear 7 to Year 12NaNNNNCoedN2015-01-27Blacktown (C)RiverstoneRiverstoneGreenwayMetropolitan NorthThe PondsNirimba9208 7611Building T3C, Nirimba Education Precinct, Eastern Rd, Quakers Hill 2763Western Sydney & Nepean Blue MountainsWestern SydneyMetropolitan WestMajor Cities of Australia-33.705259150.906931Western SydneySydney - Blacktown2023-08-18
2207891270235.0Aurora College100 Eton RoadLINDFIELD2070.01300 287 629auroracoll-h.school@det.nsw.edu.auhttp://www.aurora.nsw.edu.au02 0000 0000NaNNaNNaNNaNSecondary SchoolPartially SelectiveYOtherYear 7 to Year 12NaNNNNCoedN2015-01-27Ku-ring-gai (A)DavidsonDavidsonBradfieldMetropolitan NorthHornsbyMacPark9886 7000Level 2, 75 Talavera Rd, Macquarie Park 2113South Eastern Sydney, Northern Sydney & SydneySydneyMetropolitan NorthMajor Cities of Australia-33.789700151.160700Northern SydneySydney - North Sydney and Hornsby2023-08-18
2208891386429.0Inner Sydney High SchoolCnr Cleveland St & Chalmers StSurry Hills2010.09578 2020innersydney-h.school@det.nsw.edu.auhttps://innersydneyhighschool.schools.nsw.gov.auNaN591.03.043.01113.0Secondary SchoolNot SelectiveNComprehensiveYear 7 to Year 12NaNNNNCoedN2020-01-28Sydney (C)SydneyNewtownSydneyMetropolitan SouthPort JacksonSt Peters9582 5800Church St, St Peters 2044South Western SydneySouth Western SydneyNaNMajor Cities of Australia-33.889372151.206030NaNSydney - City and Inner South2023-08-18
22098914NaNCentre of Excellence in Agricultural EducationWSU Hawkesbury CampusRichmond2753.00400 718 234NaNhttps://richmondagcollege-h.schools.nsw.gov.auNaNNaNNaNNaNNaNSecondary SchoolNot SelectiveNAgriculturalYear 7 to Year 12NaNNNNCoedNNaTHawkesbury (C)HawkesburyHawkesburyMacquarieRegional NorthHawkesburyNirimba9208 7611Building T3C, Nirimba Education Precinct, Eastern Rd, Quakers Hill 2763Western Sydney & Nepean Blue MountainsNepean Blue MountainsNaNMajor Cities of Australia-33.614026150.753568NaNSydney - Outer West and Blue Mountains2023-08-18
2210891546436.0Armidale Secondary College182 Butler StreetArmidale2350.002 6776 7400armidale-s.school@det.nsw.edu.auhttps://armidale-s.schools.nsw.gov.auNaN1123.018.022.0961.0Secondary SchoolPartially SelectiveNComprehensiveYear 7 to Year 12NaNNNYCoedN2019-01-29Armidale Regional (A)Northern TablelandsNorthern TablelandsNew EnglandRural NorthArmidaleArmidale6776 4100Suites 2 & 3, North Power Building, 175 Rusden St ArmidaleHunter New England & Central CoastHunter New EnglandNorth Western 2Inner Regional Australia-30.519371151.650348NaNNew England and North West2023-08-18
2211891686431.0Oran Park High School1 Podium WayORAN PARK2570.04634 7700oranpark-h.school@det.nsw.edu.auhttps://oranpark-h.schools.nsw.gov.auNaN895.06.052.0995.0Secondary SchoolNot SelectiveNComprehensiveYear 7 to Year 12NaNNNNCoedN2020-01-28Camden (A)Badgerys CreekCamdenMacarthurRegional SouthMacarthurGlenfield9203 9900Roy Watts Rd, Glenfield 2167South Western SydneySouth Western SydneyNaNMajor Cities of Australia-33.998988150.736305NaNSydney - South West2023-08-18
2212891750547.0Murrumbidgee Regional High School1-39 Poole StreetGriffith2680.002 6969 9300murrumbidgee-h.school@det.nsw.edu.auhttps://murrumbidgee-h.schools.nsw.gov.auNaN1045.016.027.0918.0Secondary SchoolNot SelectiveNComprehensiveYear 7 to Year 12NaNNNNCoedN2019-01-29Griffith (C)MurrayMurrayFarrerRural South and WestGriffithGriffith02 6961 8100Government Offices, 104-110 Banna Ave, Griffith 2680Murrumbidgee, Far West & Western NSWMurrumbidgeeRiverina 1Outer Regional Australia-34.282315146.070718NaNRiverina2023-08-18
2213891988427.0Bungendore High SchoolCnr Majara Street & Kings HwyBungendore2621.0NaNbungendore-h.school@det.nsw.edu.auhttps://bungendore-h.schools.nsw.gov.au/NaNNaNNaNNaNNaNSecondary SchoolNot SelectiveNComprehensiveYear 7 to Year 12NaNNNNCoedN2023-01-27Queanbeyan-Palerang Regional (A)MonaroNaNNaNRural South and WestQueanbeyanQueanbeyan02 6200 5000Level 1, City Link Plaza, 24-36 Morisset St, Queanbeyan 2620NaNNaNNaNInner Regional Australia-35.253603149.445707NaNCapital Region2023-08-18
2214892288435.0Jerrabomberra High SchoolCoachwood AveJerrabomberra2619.0NaNjerra-h.school@det.nsw.edu.auhttps://jerra-h.schools.nsw.gov.au/NaNNaNNaNNaNNaNSecondary SchoolNot SelectiveNComprehensiveYear 7 to Year 12NaNNNNCoedN2023-01-27Queanbeyan-Palerang Regional (A)MonaroNaNNaNRural South and WestQueanbeyanQueanbeyan02 6200 5000Level 1, City Link Plaza, 24-36 Morisset St, Queanbeyan 2620NaNNaNNaNMajor Cities of Australia-35.388310149.193594NaNCapital Region2023-08-18
2215892488561.0Jindabyne High School8/20 Park RdJindabyne2627.06456 2346jindabyne-h.school@det.nsw.edu.auNot currently availableNaNNaNNaNNaNNaNSecondary SchoolNot SelectiveNComprehensiveYear 7 to Year 12NaNNNNCoedN2023-07-17_No Mapping_MonaroMonaroEden-MonaroRural South and WestEden-MonaroQueanbeyan02 6200 5000Level 1, City Link Plaza, 24-36 Morisset St, Queanbeyan 2620NaNNaNNaNNot Defined-36.416671148.619318NaNCapital Region2023-08-18